Overview

Brought to you by YData

Dataset statistics

Number of variables	35
Number of observations	7635
Missing cells	81426
Missing cells (%)	30.5%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	2.0 MiB
Average record size in memory	280.0 B

Variable types

Categorical	18
Text	16
Numeric	1

Alerts

`address` is highly overall correlated with `certification_providers` and 9 other fields	High correlation
`age_requirement` is highly overall correlated with `educationaL_requirements` and 5 other fields	High correlation
`certification_providers` is highly overall correlated with `address` and 9 other fields	High correlation
`certification_skills` is highly overall correlated with `address` and 9 other fields	High correlation
`company_urls` is highly overall correlated with `address` and 6 other fields	High correlation
`educationaL_requirements` is highly overall correlated with `age_requirement` and 5 other fields	High correlation
`experiencere_requirement` is highly overall correlated with `age_requirement` and 5 other fields	High correlation
`expiry_dates` is highly overall correlated with `address` and 8 other fields	High correlation
`extra_curricular_organization_links` is highly overall correlated with `address` and 8 other fields	High correlation
`issue_dates` is highly overall correlated with `address` and 9 other fields	High correlation
`languages` is highly overall correlated with `address` and 9 other fields	High correlation
`online_links` is highly overall correlated with `address` and 7 other fields	High correlation
`proficiency_levels` is highly overall correlated with `address` and 9 other fields	High correlation
`responsibilities` is highly overall correlated with `age_requirement` and 5 other fields	High correlation
`responsibilities.1` is highly overall correlated with `age_requirement` and 5 other fields	High correlation
`result_types` is highly overall correlated with `address` and 6 other fields	High correlation
`skills_required` is highly overall correlated with `age_requirement` and 5 other fields	High correlation
`job_position_name` is highly overall correlated with `age_requirement` and 5 other fields	High correlation
`address` has 7002 (91.7%) missing values	Missing
`career_objective` has 3841 (50.3%) missing values	Missing
`extra_curricular_activity_types` has 4903 (64.2%) missing values	Missing
`extra_curricular_organization_names` has 4903 (64.2%) missing values	Missing
`extra_curricular_organization_links` has 4903 (64.2%) missing values	Missing
`role_positions` has 4903 (64.2%) missing values	Missing
`languages` has 7066 (92.5%) missing values	Missing
`proficiency_levels` has 7066 (92.5%) missing values	Missing
`certification_providers` has 6048 (79.2%) missing values	Missing
`certification_skills` has 6048 (79.2%) missing values	Missing
`online_links` has 6048 (79.2%) missing values	Missing
`issue_dates` has 6048 (79.2%) missing values	Missing
`expiry_dates` has 6048 (79.2%) missing values	Missing
`experiencere_requirement` has 1086 (14.2%) missing values	Missing
`age_requirement` has 3264 (42.8%) missing values	Missing
`skills_required` has 1371 (18.0%) missing values	Missing
`address` is uniformly distributed	Uniform
`responsibilities` is uniformly distributed	Uniform
`job_position_name` is uniformly distributed	Uniform
`responsibilities.1` is uniformly distributed	Uniform
`skills_required` is uniformly distributed	Uniform

Reproduction

Analysis started	2025-01-03 09:55:47.789167
Analysis finished	2025-01-03 09:55:57.359703
Duration	9.57 seconds
Software version	ydata-profiling vv4.12.1
Download configuration	config.json

address
Categorical

High correlation Missing Uniform

Distinct	28
Distinct (%)	4.4%
Missing	7002
Missing (%)	91.7%
Memory size	59.8 KiB

Greer, SC	27
Denver, CO	26
Portland, OR	25
P.O BOX 2200 ELDORET	25
Hoboken, NJ	25
Other values (23)	505

Length

Max length	110
Median length	50
Mean length	28.7109
Min length	9

Characters and Unicode

Total characters	18174
Distinct characters	62
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Sunnyvale, CA
2nd row	Sunnyvale, CA
3rd row	Trenton, NJ
4th row	10 King's College Road, Rm. 3302 Toronto, Ontario, M5S 3G4 Canada
5th row	City, State Zip Code

Common Values

Value	Count	Frequency (%)
Greer, SC	27	0.4%
Denver, CO	26	0.3%
Portland, OR	25	0.3%
P.O BOX 2200 ELDORET	25	0.3%
Hoboken, NJ	25	0.3%
139 Alder Drive, Mississaugua, Ontario, L5N 6P1	25	0.3%
Street name, City, YO1 5DD	24	0.3%
10 King's College Road, Rm. 3302 Toronto, Ontario, M5S 3G4 Canada	24	0.3%
Sunnyvale, CA	24	0.3%
1234 Thundar Lane S, Fargo, ND 58102	23	0.3%
Other values (18)	385	5.0%
(Missing)	7002	91.7%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
of	91	3.1%
university	68	2.3%
street	67	2.3%
city	66	2.2%
ontario	49	1.7%
canada	47	1.6%
nj	46	1.6%
drive	46	1.6%
computer	45	1.5%
science	45	1.5%
Other values (101)	2379	80.7%

Most occurring characters

Value	Count	Frequency (%)
	2316	12.7%
e	1391	7.7%
,	997	5.5%
a	931	5.1%
r	888	4.9%
n	883	4.9%
t	827	4.6%
i	787	4.3%
o	701	3.9%
C	431	2.4%
Other values (52)	8022	44.1%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	18174	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	2316	12.7%
e	1391	7.7%
,	997	5.5%
a	931	5.1%
r	888	4.9%
n	883	4.9%
t	827	4.6%
i	787	4.3%
o	701	3.9%
C	431	2.4%
Other values (52)	8022	44.1%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	18174	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	2316	12.7%
e	1391	7.7%
,	997	5.5%
a	931	5.1%
r	888	4.9%
n	883	4.9%
t	827	4.6%
i	787	4.3%
o	701	3.9%
C	431	2.4%
Other values (52)	8022	44.1%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	18174	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	2316	12.7%
e	1391	7.7%
,	997	5.5%
a	931	5.1%
r	888	4.9%
n	883	4.9%
t	827	4.6%
i	787	4.3%
o	701	3.9%
C	431	2.4%
Other values (52)	8022	44.1%

career_objective
Text

Missing

Distinct	171
Distinct (%)	4.5%
Missing	3841
Missing (%)	50.3%
Memory size	59.8 KiB

Length

Max length	1425
Median length	259
Mean length	226.45809
Min length	26

Characters and Unicode

Total characters	859182
Distinct characters	74
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Big data analytics working and database warehouse manager with robust experience in handling all kinds of data. I have also used multiple cloud infrastructure services and am well acquainted with them. Currently in search of role that offers more of development.
2nd row	Fresher looking to join as a data analyst and junior data scientist. Experienced in creating meaningful data dashboards and evaluation models.
3rd row	To obtain a position in a fast-paced business office environment, demanding a strong organizational, technical, and interpersonal position utilizing my skills and attributes.
4th row	Professional accountant with an outstanding work ethic and integrity seeking to make a valuable contribution utilizing strong analytical, organizational, communication, and computer skills.
5th row	To secure an IT specialist, desktop support, network administration, database administrator, technical support specialist or related position with a growing organization where my Microsoft certification, technical aptitude, networking, Windows and Mac OS, Apple and Android IOS, web development, application development, Linux, Microsoft applications, managing, testing, client support, help desk, technical support, troubleshooting, and leadership skills can benefit those who I work for as well as myself.

Value	Count	Frequency (%)
and	7912	6.2%
to	4868	3.8%
a	4301	3.4%
in	3484	2.7%
i	2690	2.1%
the	2461	1.9%
of	2426	1.9%
with	2311	1.8%
data	2255	1.8%
learning	2234	1.7%
Other values (1219)	92799	72.6%

Most occurring characters

Value	Count	Frequency (%)
	123971	14.4%
e	81424	9.5%
n	69509	8.1%
a	62938	7.3%
i	61133	7.1%
t	53070	6.2%
o	51283	6.0%
r	41286	4.8%
s	38692	4.5%
l	32491	3.8%
Other values (64)	243385	28.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	859182	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	123971	14.4%
e	81424	9.5%
n	69509	8.1%
a	62938	7.3%
i	61133	7.1%
t	53070	6.2%
o	51283	6.0%
r	41286	4.8%
s	38692	4.5%
l	32491	3.8%
Other values (64)	243385	28.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	859182	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	123971	14.4%
e	81424	9.5%
n	69509	8.1%
a	62938	7.3%
i	61133	7.1%
t	53070	6.2%
o	51283	6.0%
r	41286	4.8%
s	38692	4.5%
l	32491	3.8%
Other values (64)	243385	28.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	859182	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	123971	14.4%
e	81424	9.5%
n	69509	8.1%
a	62938	7.3%
i	61133	7.1%
t	53070	6.2%
o	51283	6.0%
r	41286	4.8%
s	38692	4.5%
l	32491	3.8%
Other values (64)	243385	28.3%

skills
Text

Distinct	340
Distinct (%)	4.5%
Missing	43
Missing (%)	0.6%
Memory size	59.8 KiB

Length

Max length	3104
Median length	477
Mean length	370.9053
Min length	2

Characters and Unicode

Total characters	2815913
Distinct characters	82
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	< 0.1%

Sample

1st row	['Big Data', 'Hadoop', 'Hive', 'Python', 'Mapreduce', 'Spark', 'Java', 'Machine Learning', 'Cloud', 'Hdfs', 'YARN', 'Core Java', 'Data Science', 'C++', 'Data Structures', 'DBMS', 'RDBMS', 'Informatica', 'Talend', 'Amazon Redshift', 'Microsoft Azure']
2nd row	['Data Analysis', 'Data Analytics', 'Business Analysis', 'R', 'SAS', 'PowerBi', 'Tableau', 'Data Visualization', 'Business Analytics', 'Machine Learning']
3rd row	['Software Development', 'Machine Learning', 'Deep Learning', 'Risk Assessment', 'Requirement Gathering', 'Application Support', 'JavaScript', 'Python', 'Docker', 'HTML', 'Hive', 'CSS', 'C', 'C++']
4th row	['accounts payables', 'accounts receivables', 'Accounts Payable', 'Accounts Receivable', 'administrative functions', 'trial balance', 'banking', 'budget', 'bi', 'closing', 'Computer Applications', 'Credit', 'clients', 'Customer Service', 'data entry', 'delivery', 'driving', 'email', 'insurance', 'inventory', 'ledger', 'Access', 'Excel', 'Outlook', 'PowerPoint', 'Word', 'mortgage loan', 'Enterprise', 'policies', 'QuickBooks', 'Sales', 'sales reports', 'telecommunications', 'phone', 'workflow', 'written']
5th row	['Analytical reasoning', 'Compliance testing knowledge', 'Effective time management', 'Public and private accounting', 'accounting', 'accounting systems', 'accounts payable', 'accounts receivable', 'administrative', 'AR', 'billing', 'closing', 'client', 'clients', 'documentation', 'financial', 'financial reports', 'preparation of financial reports', 'Preparation of financial statements', 'fixed assets', 'managing', 'month-end closing', 'policies', 'maintain records', 'reporting', 'Research', 'sales', 'tax', 'taxes', 'tax returns', 'annual reports', 'year-end']

Value	Count	Frequency (%)
data	6752	2.4%
management	4977	1.7%
and	4604	1.6%
learning	4292	1.5%
analysis	4180	1.5%
microsoft	3171	1.1%
python	3098	1.1%
machine	2926	1.0%
office	2676	0.9%
financial	2269	0.8%
Other values (2561)	248064	86.4%

Most occurring characters

Value	Count	Frequency (%)
'	329711	11.7%
	279417	9.9%
e	200034	7.1%
n	169862	6.0%
i	165867	5.9%
,	164593	5.8%
a	161999	5.8%
t	142933	5.1%
o	121299	4.3%
s	119461	4.2%
Other values (72)	960737	34.1%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	2815913	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
'	329711	11.7%
	279417	9.9%
e	200034	7.1%
n	169862	6.0%
i	165867	5.9%
,	164593	5.8%
a	161999	5.8%
t	142933	5.1%
o	121299	4.3%
s	119461	4.2%
Other values (72)	960737	34.1%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	2815913	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
'	329711	11.7%
	279417	9.9%
e	200034	7.1%
n	169862	6.0%
i	165867	5.9%
,	164593	5.8%
a	161999	5.8%
t	142933	5.1%
o	121299	4.3%
s	119461	4.2%
Other values (72)	960737	34.1%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	2815913	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
'	329711	11.7%
	279417	9.9%
e	200034	7.1%
n	169862	6.0%
i	165867	5.9%
,	164593	5.8%
a	161999	5.8%
t	142933	5.1%
o	121299	4.3%
s	119461	4.2%
Other values (72)	960737	34.1%

educational_institution_name
Text

Distinct	328
Distinct (%)	4.3%
Missing	61
Missing (%)	0.8%
Memory size	59.8 KiB

Length

Max length	212
Median length	91
Mean length	48.523633
Min length	8

Characters and Unicode

Total characters	367518
Distinct characters	67
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	< 0.1%

Sample

1st row	['The Amity School of Engineering & Technology (ASET), Noida']
2nd row	['Delhi University - Hansraj College', 'Delhi University - Hansraj College']
3rd row	['Birla Institute of Technology (BIT), Ranchi']
4th row	['Martinez Adult Education, Business Training Center ï¼ City , State']
5th row	['Kent State University']

Value	Count	Frequency (%)
university	6534	15.3%
of	3679	8.6%
college	2419	5.7%
technology	1150	2.7%
institute	1140	2.7%
school	878	2.1%
state	830	1.9%
engineering	567	1.3%
and	479	1.1%
high	433	1.0%
Other values (638)	24668	57.7%

Most occurring characters

Value	Count	Frequency (%)
	35203	9.6%
e	24868	6.8%
i	24733	6.7%
'	24625	6.7%
n	20308	5.5%
a	17323	4.7%
o	17211	4.7%
t	17067	4.6%
r	15986	4.3%
l	12665	3.4%
Other values (57)	157529	42.9%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	367518	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	35203	9.6%
e	24868	6.8%
i	24733	6.7%
'	24625	6.7%
n	20308	5.5%
a	17323	4.7%
o	17211	4.7%
t	17067	4.6%
r	15986	4.3%
l	12665	3.4%
Other values (57)	157529	42.9%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	367518	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	35203	9.6%
e	24868	6.8%
i	24733	6.7%
'	24625	6.7%
n	20308	5.5%
a	17323	4.7%
o	17211	4.7%
t	17067	4.6%
r	15986	4.3%
l	12665	3.4%
Other values (57)	157529	42.9%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	367518	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	35203	9.6%
e	24868	6.8%
i	24733	6.7%
'	24625	6.7%
n	20308	5.5%
a	17323	4.7%
o	17211	4.7%
t	17067	4.6%
r	15986	4.3%
l	12665	3.4%
Other values (57)	157529	42.9%

degree_names
Text

Distinct	180
Distinct (%)	2.4%
Missing	61
Missing (%)	0.8%
Memory size	59.8 KiB

Length

Max length	472
Median length	125
Mean length	30.597703
Min length	6

Characters and Unicode

Total characters	231747
Distinct characters	75
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	< 0.1%

Sample

1st row	['B.Tech']
2nd row	['B.Sc (Maths)', 'M.Sc (Science) (Statistics)']
3rd row	['B.Tech']
4th row	['Computer Applications Specialist Certificate Program']
5th row	['Bachelor of Business Administration']

Value	Count	Frequency (%)
of	3740	13.7%
science	2876	10.6%
bachelor	2479	9.1%
b.tech	2167	8.0%
master	805	3.0%
business	620	2.3%
diploma	597	2.2%
administration	500	1.8%
degree	481	1.8%
computer	432	1.6%
Other values (232)	12543	46.0%

Most occurring characters

Value	Count	Frequency (%)
'	24471	10.6%
e	19917	8.6%
	19666	8.5%
c	14772	6.4%
o	12018	5.2%
i	10730	4.6%
n	9257	4.0%
a	8928	3.9%
B	8354	3.6%
r	8129	3.5%
Other values (65)	95505	41.2%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	231747	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
'	24471	10.6%
e	19917	8.6%
	19666	8.5%
c	14772	6.4%
o	12018	5.2%
i	10730	4.6%
n	9257	4.0%
a	8928	3.9%
B	8354	3.6%
r	8129	3.5%
Other values (65)	95505	41.2%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	231747	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
'	24471	10.6%
e	19917	8.6%
	19666	8.5%
c	14772	6.4%
o	12018	5.2%
i	10730	4.6%
n	9257	4.0%
a	8928	3.9%
B	8354	3.6%
r	8129	3.5%
Other values (65)	95505	41.2%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	231747	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
'	24471	10.6%
e	19917	8.6%
	19666	8.5%
c	14772	6.4%
o	12018	5.2%
i	10730	4.6%
n	9257	4.0%
a	8928	3.9%
B	8354	3.6%
r	8129	3.5%
Other values (65)	95505	41.2%

passing_years
Text

Distinct	149
Distinct (%)	2.0%
Missing	61
Missing (%)	0.8%
Memory size	59.8 KiB

Length

Max length	121
Median length	71
Mean length	13.737391
Min length	6

Characters and Unicode

Total characters	104047
Distinct characters	50
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	< 0.1%

Sample

1st row	['2019']
2nd row	['2015', '2018']
3rd row	['2018']
4th row	['2008']
5th row	[None]

Value	Count	Frequency (%)
2019	1711	12.9%
n/a	1241	9.3%
2020	1210	9.1%
none	781	5.9%
2018	615	4.6%
2017	598	4.5%
2021	455	3.4%
2010	445	3.3%
2014	396	3.0%
2004	320	2.4%
Other values (77)	5527	41.6%

Most occurring characters

Value	Count	Frequency (%)
'	23466	22.6%
0	13427	12.9%
2	11502	11.1%
1	7612	7.3%
[	7574	7.3%
]	7574	7.3%
	5725	5.5%
,	4962	4.8%
9	3908	3.8%
N	2022	1.9%
Other values (40)	16275	15.6%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	104047	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
'	23466	22.6%
0	13427	12.9%
2	11502	11.1%
1	7612	7.3%
[	7574	7.3%
]	7574	7.3%
	5725	5.5%
,	4962	4.8%
9	3908	3.8%
N	2022	1.9%
Other values (40)	16275	15.6%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	104047	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
'	23466	22.6%
0	13427	12.9%
2	11502	11.1%
1	7612	7.3%
[	7574	7.3%
]	7574	7.3%
	5725	5.5%
,	4962	4.8%
9	3908	3.8%
N	2022	1.9%
Other values (40)	16275	15.6%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	104047	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
'	23466	22.6%
0	13427	12.9%
2	11502	11.1%
1	7612	7.3%
[	7574	7.3%
]	7574	7.3%
	5725	5.5%
,	4962	4.8%
9	3908	3.8%
N	2022	1.9%
Other values (40)	16275	15.6%

educational_results
Text

Distinct	78
Distinct (%)	1.0%
Missing	61
Missing (%)	0.8%
Memory size	59.8 KiB

Length

Max length	102
Median length	73
Mean length	12.611434
Min length	6

Characters and Unicode

Total characters	95519
Distinct characters	62
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	['N/A']
2nd row	['N/A', 'N/A']
3rd row	['N/A']
4th row	[None]
5th row	['3.84']

Value	Count	Frequency (%)
n/a	7016	50.0%
none	3172	22.6%
4.0	204	1.5%
a	144	1.0%
3.7	141	1.0%
3.8	119	0.8%
3.5	117	0.8%
cum	105	0.7%
laude	105	0.7%
3.6	94	0.7%
Other values (88)	2808	20.0%

Most occurring characters

Value	Count	Frequency (%)
'	18639	19.5%
N	10188	10.7%
[	7574	7.9%
]	7574	7.9%
A	7250	7.6%
/	7206	7.5%
	6451	6.8%
,	4988	5.2%
n	3854	4.0%
e	3833	4.0%
Other values (52)	17962	18.8%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	95519	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
'	18639	19.5%
N	10188	10.7%
[	7574	7.9%
]	7574	7.9%
A	7250	7.6%
/	7206	7.5%
	6451	6.8%
,	4988	5.2%
n	3854	4.0%
e	3833	4.0%
Other values (52)	17962	18.8%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	95519	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
'	18639	19.5%
N	10188	10.7%
[	7574	7.9%
]	7574	7.9%
A	7250	7.6%
/	7206	7.5%
	6451	6.8%
,	4988	5.2%
n	3854	4.0%
e	3833	4.0%
Other values (52)	17962	18.8%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	95519	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
'	18639	19.5%
N	10188	10.7%
[	7574	7.9%
]	7574	7.9%
A	7250	7.6%
/	7206	7.5%
	6451	6.8%
,	4988	5.2%
n	3854	4.0%
e	3833	4.0%
Other values (52)	17962	18.8%

result_types
Categorical

High correlation

Distinct	30
Distinct (%)	0.4%
Missing	61
Missing (%)	0.8%
Memory size	59.8 KiB

['N/A']	2704
['N/A', 'N/A']	1245
[None]	885
[None, None]	762
['GPA']	541
Other values (25)	1437

Length

Max length	94
Median length	66
Mean length	11.405466
Min length	6

Characters and Unicode

Total characters	86385
Distinct characters	34
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	[None]
2nd row	['N/A', 'N/A']
3rd row	['N/A']
4th row	[None]
5th row	[None]

Common Values

Value	Count	Frequency (%)
['N/A']	2704	35.4%
['N/A', 'N/A']	1245	16.3%
[None]	885	11.6%
[None, None]	762	10.0%
['GPA']	541	7.1%
[None, None, None]	247	3.2%
['N/A', 'N/A', 'N/A']	245	3.2%
['GPA', 'GPA']	223	2.9%
['N/A', 'N/A', 'N/A', 'N/A']	88	1.2%
['GPA', None]	87	1.1%
Other values (20)	547	7.2%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
n/a	6686	52.4%
none	4095	32.1%
gpa	1445	11.3%
percentage	114	0.9%
cgpa	69	0.5%
honors	61	0.5%
every	44	0.3%
quarter	44	0.3%
major	23	0.2%
highest	22	0.2%
Other values (7)	154	1.2%

Most occurring characters

Value	Count	Frequency (%)
'	16838	19.5%
N	10781	12.5%
A	8244	9.5%
[	7574	8.8%
]	7574	8.8%
/	6686	7.7%
	5183	6.0%
,	4940	5.7%
e	4723	5.5%
n	4358	5.0%
Other values (24)	9484	11.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	86385	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
'	16838	19.5%
N	10781	12.5%
A	8244	9.5%
[	7574	8.8%
]	7574	8.8%
/	6686	7.7%
	5183	6.0%
,	4940	5.7%
e	4723	5.5%
n	4358	5.0%
Other values (24)	9484	11.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	86385	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
'	16838	19.5%
N	10781	12.5%
A	8244	9.5%
[	7574	8.8%
]	7574	8.8%
/	6686	7.7%
	5183	6.0%
,	4940	5.7%
e	4723	5.5%
n	4358	5.0%
Other values (24)	9484	11.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	86385	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
'	16838	19.5%
N	10781	12.5%
A	8244	9.5%
[	7574	8.8%
]	7574	8.8%
/	6686	7.7%
	5183	6.0%
,	4940	5.7%
e	4723	5.5%
n	4358	5.0%
Other values (24)	9484	11.0%

major_field_of_studies
Text

Distinct	210
Distinct (%)	2.8%
Missing	61
Missing (%)	0.8%
Memory size	59.8 KiB

Length

Max length	260
Median length	95
Mean length	35.225376
Min length	6

Characters and Unicode

Total characters	266797
Distinct characters	60
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	< 0.1%

Sample

1st row	['Electronics']
2nd row	['Mathematics', 'Statistics']
3rd row	['Electronics/Telecommunication']
4th row	['Computer Applications']
5th row	['Accounting']

Value	Count	Frequency (%)
engineering	2067	8.2%
computer	1811	7.2%
science	1679	6.6%
n/a	1407	5.6%
and	1222	4.8%
accounting	970	3.8%
none	758	3.0%
technology	746	2.9%
management	736	2.9%
business	726	2.9%
Other values (232)	13201	52.1%

Most occurring characters

Value	Count	Frequency (%)
n	24374	9.1%
'	23512	8.8%
e	21309	8.0%
i	18676	7.0%
	17749	6.7%
c	14486	5.4%
o	12445	4.7%
t	12413	4.7%
a	11934	4.5%
r	10179	3.8%
Other values (50)	99720	37.4%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	266797	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
n	24374	9.1%
'	23512	8.8%
e	21309	8.0%
i	18676	7.0%
	17749	6.7%
c	14486	5.4%
o	12445	4.7%
t	12413	4.7%
a	11934	4.5%
r	10179	3.8%
Other values (50)	99720	37.4%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	266797	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
n	24374	9.1%
'	23512	8.8%
e	21309	8.0%
i	18676	7.0%
	17749	6.7%
c	14486	5.4%
o	12445	4.7%
t	12413	4.7%
a	11934	4.5%
r	10179	3.8%
Other values (50)	99720	37.4%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	266797	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
n	24374	9.1%
'	23512	8.8%
e	21309	8.0%
i	18676	7.0%
	17749	6.7%
c	14486	5.4%
o	12445	4.7%
t	12413	4.7%
a	11934	4.5%
r	10179	3.8%
Other values (50)	99720	37.4%

professional_company_names
Text

Distinct	199
Distinct (%)	2.6%
Missing	67
Missing (%)	0.9%
Memory size	59.8 KiB

Length

Max length	266
Median length	135
Mean length	49.603066
Min length	6

Characters and Unicode

Total characters	375396
Distinct characters	69
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	['Coca-COla']
2nd row	['BIB Consultancy']
3rd row	['Axis Bank Limited']
4th row	['Company Name ï¼ City , State', 'Company Name ï¼ City , State', 'Company Name ï¼ City , State', 'Company Name ï¼ City , State', 'Company Name ï¼ City , State', 'N/A']
5th row	['Company Name', 'Company Name', 'Company Name', 'Company Name', 'Company Name']

Value	Count	Frequency (%)
company	14400	31.2%
name	14334	31.0%
of	732	1.6%
university	646	1.4%
ltd	507	1.1%
n/a	388	0.8%
	377	0.8%
pvt	342	0.7%
science	255	0.6%
and	251	0.5%
Other values (425)	13964	30.2%

Most occurring characters

Value	Count	Frequency (%)
'	42370	11.3%
	38628	10.3%
a	35296	9.4%
m	30614	8.2%
e	23308	6.2%
o	21459	5.7%
n	21347	5.7%
C	16795	4.5%
y	16380	4.4%
p	15892	4.2%
Other values (59)	113307	30.2%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	375396	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
'	42370	11.3%
	38628	10.3%
a	35296	9.4%
m	30614	8.2%
e	23308	6.2%
o	21459	5.7%
n	21347	5.7%
C	16795	4.5%
y	16380	4.4%
p	15892	4.2%
Other values (59)	113307	30.2%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	375396	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
'	42370	11.3%
	38628	10.3%
a	35296	9.4%
m	30614	8.2%
e	23308	6.2%
o	21459	5.7%
n	21347	5.7%
C	16795	4.5%
y	16380	4.4%
p	15892	4.2%
Other values (59)	113307	30.2%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	375396	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
'	42370	11.3%
	38628	10.3%
a	35296	9.4%
m	30614	8.2%
e	23308	6.2%
o	21459	5.7%
n	21347	5.7%
C	16795	4.5%
y	16380	4.4%
p	15892	4.2%
Other values (59)	113307	30.2%

company_urls
Categorical

High correlation

Distinct	16
Distinct (%)	0.2%
Missing	67
Missing (%)	0.9%
Memory size	59.8 KiB

['N/A']	1944
[None, None, None]	1164
[None, None, None, None]	1049
[None]	1024
[None, None]	527
Other values (11)	1860

Length

Max length	277
Median length	48
Mean length	18.033034
Min length	6

Characters and Unicode

Total characters	136474
Distinct characters	29
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	[None]
2nd row	['N/A']
3rd row	['N/A']
4th row	[None, None, None, None, None, None]
5th row	[None, None, None, None, None]

Common Values

Value	Count	Frequency (%)
['N/A']	1944	25.5%
[None, None, None]	1164	15.2%
[None, None, None, None]	1049	13.7%
[None]	1024	13.4%
[None, None]	527	6.9%
[None, None, None, None, None]	453	5.9%
[None, None, None, None, None, None]	356	4.7%
['N/A', 'N/A', 'N/A']	269	3.5%
['N/A', 'N/A']	262	3.4%
[None, None, None, None, None, None, None]	243	3.2%
Other values (6)	277	3.6%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
none	17273	81.4%
n/a	3723	17.5%
https://www.calero.com	25	0.1%
https://www.clacorp.com	25	0.1%
https://www.pictureu.com	25	0.1%
https://www.ticketalternative.com	25	0.1%
https://www.triplingo.com	25	0.1%
https://www.deposco.com	25	0.1%
https://www.servigistics.com	25	0.1%
https://www.commercescience.com	25	0.1%

Most occurring characters

Value	Count	Frequency (%)
N	20996	15.4%
o	17673	12.9%
e	17598	12.9%
n	17373	12.7%
,	13653	10.0%
	13653	10.0%
'	7896	5.8%
[	7568	5.5%
]	7568	5.5%
/	4173	3.1%
Other values (19)	8323	6.1%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	136474	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
N	20996	15.4%
o	17673	12.9%
e	17598	12.9%
n	17373	12.7%
,	13653	10.0%
	13653	10.0%
'	7896	5.8%
[	7568	5.5%
]	7568	5.5%
/	4173	3.1%
Other values (19)	8323	6.1%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	136474	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
N	20996	15.4%
o	17673	12.9%
e	17598	12.9%
n	17373	12.7%
,	13653	10.0%
	13653	10.0%
'	7896	5.8%
[	7568	5.5%
]	7568	5.5%
/	4173	3.1%
Other values (19)	8323	6.1%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	136474	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
N	20996	15.4%
o	17673	12.9%
e	17598	12.9%
n	17373	12.7%
,	13653	10.0%
	13653	10.0%
'	7896	5.8%
[	7568	5.5%
]	7568	5.5%
/	4173	3.1%
Other values (19)	8323	6.1%

start_dates
Text

Distinct	252
Distinct (%)	3.3%
Missing	67
Missing (%)	0.9%
Memory size	59.8 KiB

Length

Max length	129
Median length	112
Mean length	34.998943
Min length	6

Characters and Unicode

Total characters	264872
Distinct characters	50
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	< 0.1%

Sample

1st row	['Nov 2019']
2nd row	['Sep 2019']
3rd row	['June 2018']
4th row	['January 2011', 'January 2008', 'January 2006', 'January 2004', 'January 2001', 'N/A']
5th row	['January 2016', 'January 2016', 'January 2012', 'January 2009', 'January 2006']

Value	Count	Frequency (%)
2019	2032	6.1%
january	1982	5.9%
2020	1217	3.6%
may	1024	3.1%
jan	995	3.0%
june	749	2.2%
2018	729	2.2%
august	690	2.1%
2011	677	2.0%
july	671	2.0%
Other values (271)	22778	67.9%

Most occurring characters

Value	Count	Frequency (%)
'	42226	15.9%
0	33135	12.5%
	25976	9.8%
2	21958	8.3%
1	18551	7.0%
,	13713	5.2%
9	9302	3.5%
/	7928	3.0%
]	7568	2.9%
[	7568	2.9%
Other values (40)	76947	29.1%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	264872	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
'	42226	15.9%
0	33135	12.5%
	25976	9.8%
2	21958	8.3%
1	18551	7.0%
,	13713	5.2%
9	9302	3.5%
/	7928	3.0%
]	7568	2.9%
[	7568	2.9%
Other values (40)	76947	29.1%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	264872	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
'	42226	15.9%
0	33135	12.5%
	25976	9.8%
2	21958	8.3%
1	18551	7.0%
,	13713	5.2%
9	9302	3.5%
/	7928	3.0%
]	7568	2.9%
[	7568	2.9%
Other values (40)	76947	29.1%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	264872	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
'	42226	15.9%
0	33135	12.5%
	25976	9.8%
2	21958	8.3%
1	18551	7.0%
,	13713	5.2%
9	9302	3.5%
/	7928	3.0%
]	7568	2.9%
[	7568	2.9%
Other values (40)	76947	29.1%

end_dates
Text

Distinct	246
Distinct (%)	3.3%
Missing	67
Missing (%)	0.9%
Memory size	59.8 KiB

Length

Max length	133
Median length	105
Mean length	34.138478
Min length	6

Characters and Unicode

Total characters	258360
Distinct characters	55
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	< 0.1%

Sample

1st row	['Till Date']
2nd row	['Till Date']
3rd row	['Till Date']
4th row	['November 2015', 'January 2010', 'January 2008', 'January 2006', 'January 2004', None]
5th row	['Current', 'January 2016', 'January 2015', 'January 2011', 'January 2009']

Value	Count	Frequency (%)
current	2305	7.4%
january	1587	5.1%
date	1123	3.6%
till	1123	3.6%
2020	1076	3.4%
may	820	2.6%
august	651	2.1%
2014	617	2.0%
none	572	1.8%
ongoing	564	1.8%
Other values (233)	20904	66.7%

Most occurring characters

Value	Count	Frequency (%)
'	41298	16.0%
0	26636	10.3%
	23774	9.2%
2	18077	7.0%
1	13815	5.3%
,	13713	5.3%
r	9484	3.7%
e	9410	3.6%
]	7568	2.9%
[	7568	2.9%
Other values (45)	87017	33.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	258360	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
'	41298	16.0%
0	26636	10.3%
	23774	9.2%
2	18077	7.0%
1	13815	5.3%
,	13713	5.3%
r	9484	3.7%
e	9410	3.6%
]	7568	2.9%
[	7568	2.9%
Other values (45)	87017	33.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	258360	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
'	41298	16.0%
0	26636	10.3%
	23774	9.2%
2	18077	7.0%
1	13815	5.3%
,	13713	5.3%
r	9484	3.7%
e	9410	3.6%
]	7568	2.9%
[	7568	2.9%
Other values (45)	87017	33.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	258360	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
'	41298	16.0%
0	26636	10.3%
	23774	9.2%
2	18077	7.0%
1	13815	5.3%
,	13713	5.3%
r	9484	3.7%
e	9410	3.6%
]	7568	2.9%
[	7568	2.9%
Other values (45)	87017	33.7%

related_skils_in_job
Text

Distinct	298
Distinct (%)	3.9%
Missing	67
Missing (%)	0.9%
Memory size	59.8 KiB

Length

Max length	1138
Median length	394
Mean length	179.77762
Min length	4

Characters and Unicode

Total characters	1360557
Distinct characters	75
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	< 0.1%

Sample

1st row	[['Big Data']]
2nd row	[['Data Analysis', 'Business Analysis', 'Machine Learning']]
3rd row	[['Unified Payment Interface', 'Risk Prediction', 'Big Data', 'Spark', 'PySpark']]
4th row	[['accounts receivables', 'banking', 'G/L Accounts', 'accounts payables', 'credit cards', 'reconcile', 'commission reports', 'credit checks', 'customer service', 'international travel'], ['data entry', 'accounts receivable', 'cash handling', 'customer communication', 'inventory reports', 'problem-solving'], ['mortgage processing', 'analytical aptitude', 'credit reports', 'customer communication'], ['commercial auto underwriting', 'data entry', 'application review', 'customer communication'], ['personal auto underwriting', 'data entry', 'policy review', 'customer service'], ['training', 'medical record review', 'data entry', 'document design', 'customer service', 'team performance']]
5th row	[['collections', 'accounts receivable', 'financial reports', 'AR aging', 'customer queries', 'sales and use tax audits'], ['financial statements', 'GAAP', 'asset', 'liability', 'capital account', 'accounting controls', 'audits'], ['sales tax', 'tax returns', 'business licenses', 'annual reports', 'tax audits'], ['financial reporting', 'fixed assets', 'sales tax', 'cash projections', 'general ledger accounting'], ['audit procedures', 'substantive tests', 'internal accounting', 'tests of compliance', 'audit programs']]

Value	Count	Frequency (%)
management	4198	3.2%
none	3920	3.0%
data	2583	2.0%
analysis	2363	1.8%
and	2037	1.6%
financial	2003	1.5%
development	1543	1.2%
learning	1462	1.1%
accounting	1310	1.0%
sales	1280	1.0%
Other values (1436)	107053	82.5%

Most occurring characters

Value	Count	Frequency (%)
'	137357	10.1%
	122184	9.0%
e	104363	7.7%
n	98158	7.2%
a	83710	6.2%
i	81743	6.0%
t	75306	5.5%
,	66012	4.9%
o	64777	4.8%
s	54931	4.0%
Other values (65)	472016	34.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	1360557	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
'	137357	10.1%
	122184	9.0%
e	104363	7.7%
n	98158	7.2%
a	83710	6.2%
i	81743	6.0%
t	75306	5.5%
,	66012	4.9%
o	64777	4.8%
s	54931	4.0%
Other values (65)	472016	34.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	1360557	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
'	137357	10.1%
	122184	9.0%
e	104363	7.7%
n	98158	7.2%
a	83710	6.2%
i	81743	6.0%
t	75306	5.5%
,	66012	4.9%
o	64777	4.8%
s	54931	4.0%
Other values (65)	472016	34.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	1360557	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
'	137357	10.1%
	122184	9.0%
e	104363	7.7%
n	98158	7.2%
a	83710	6.2%
i	81743	6.0%
t	75306	5.5%
,	66012	4.9%
o	64777	4.8%
s	54931	4.0%
Other values (65)	472016	34.7%

positions
Text

Distinct	301
Distinct (%)	4.0%
Missing	67
Missing (%)	0.9%
Memory size	59.8 KiB

Length

Max length	293
Median length	148
Mean length	73.203621
Min length	7

Characters and Unicode

Total characters	554005
Distinct characters	61
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	< 0.1%

Sample

1st row	['Big Data Analyst']
2nd row	['Business Analyst']
3rd row	['Software Developer (Machine Learning Engineer)']
4th row	['Accountant', 'Accounts Receivable Clerk', 'Mortgage Underwriter', 'Commercial Auto Underwriter', 'Personal Auto Underwriter', 'Claims Examiner']
5th row	['Staff Accountant', 'Senior Accountant', 'Tax Analyst', 'Staff Accountant II', 'Staff Auditor II']

Value	Count	Frequency (%)
manager	2798	5.0%
engineering	2740	4.9%
engineer	2488	4.4%
accountant	2213	4.0%
intern	2104	3.8%
technician	1635	2.9%
analyst	1544	2.8%
assistant	1379	2.5%
software	1206	2.2%
business	1015	1.8%
Other values (489)	36879	65.9%

Most occurring characters

Value	Count	Frequency (%)
n	51894	9.4%
e	49804	9.0%
	48433	8.7%
'	42398	7.7%
i	33814	6.1%
a	33046	6.0%
t	32326	5.8%
r	31795	5.7%
o	21520	3.9%
c	21194	3.8%
Other values (51)	187781	33.9%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	554005	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
n	51894	9.4%
e	49804	9.0%
	48433	8.7%
'	42398	7.7%
i	33814	6.1%
a	33046	6.0%
t	32326	5.8%
r	31795	5.7%
o	21520	3.9%
c	21194	3.8%
Other values (51)	187781	33.9%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	554005	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
n	51894	9.4%
e	49804	9.0%
	48433	8.7%
'	42398	7.7%
i	33814	6.1%
a	33046	6.0%
t	32326	5.8%
r	31795	5.7%
o	21520	3.9%
c	21194	3.8%
Other values (51)	187781	33.9%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	554005	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
n	51894	9.4%
e	49804	9.0%
	48433	8.7%
'	42398	7.7%
i	33814	6.1%
a	33046	6.0%
t	32326	5.8%
r	31795	5.7%
o	21520	3.9%
c	21194	3.8%
Other values (51)	187781	33.9%

locations
Text

Distinct	86
Distinct (%)	1.1%
Missing	67
Missing (%)	0.9%
Memory size	59.8 KiB

Length

Max length	152
Median length	128
Mean length	36.40473
Min length	6

Characters and Unicode

Total characters	275511
Distinct characters	54
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	['N/A']
2nd row	['N/A']
3rd row	['N/A']
4th row	['City, State', 'City, State', 'City, State', 'City, State', 'City, State', 'N/A']
5th row	['City, State', 'City, State', 'City, State', 'City, State', 'City, State']

Value	Count	Frequency (%)
city	13428	32.6%
state	13033	31.6%
	4789	11.6%
n/a	4555	11.1%
none	1168	2.8%
ca	163	0.4%
fl	111	0.3%
remote	109	0.3%
tx	109	0.3%
lexington	88	0.2%
Other values (103)	3637	8.8%

Most occurring characters

Value	Count	Frequency (%)
t	40705	14.8%
'	40106	14.6%
	33622	12.2%
,	28234	10.2%
e	16022	5.8%
a	14669	5.3%
i	14550	5.3%
C	14154	5.1%
y	13605	4.9%
S	13384	4.9%
Other values (44)	46460	16.9%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	275511	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
t	40705	14.8%
'	40106	14.6%
	33622	12.2%
,	28234	10.2%
e	16022	5.8%
a	14669	5.3%
i	14550	5.3%
C	14154	5.1%
y	13605	4.9%
S	13384	4.9%
Other values (44)	46460	16.9%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	275511	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
t	40705	14.8%
'	40106	14.6%
	33622	12.2%
,	28234	10.2%
e	16022	5.8%
a	14669	5.3%
i	14550	5.3%
C	14154	5.1%
y	13605	4.9%
S	13384	4.9%
Other values (44)	46460	16.9%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	275511	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
t	40705	14.8%
'	40106	14.6%
	33622	12.2%
,	28234	10.2%
e	16022	5.8%
a	14669	5.3%
i	14550	5.3%
C	14154	5.1%
y	13605	4.9%
S	13384	4.9%
Other values (44)	46460	16.9%

responsibilities
Categorical

High correlation Uniform

Distinct	28
Distinct (%)	0.4%
Missing	0
Missing (%)	0.0%
Memory size	59.8 KiB

Database Design & Development SQL Query Optimization Data Integrity & Security BI Solutions Development ETL Process Implementation Database Maintenance Backup & Restore Management Index Rebuilding & Performance Tuning SQL Server Clustering & High Availability SQL Server Replication High Availability Group Management Database Monitoring & Troubleshooting	284
Full Stack Development Front-end: ReactJS, NextJS Backend: Python, Django API Design Server-Side Logic DRF (Django REST Framework) Database Management (PostgreSQL, MySQL) Version Control (Git) AWS (ECR, RDS, ECS, ALB, EC2, etc.) Linux, Docker, CI/CD, GitLab Terraform, Shell Scripting	283
Design Creation CAD Drawings Design Optimization Team Collaboration Compliance Assurance Design Reviews Manufacturing Support Documentation	281
Administrative Support Scheduling Filing & Documentation Communication Team Support Equipment Maintenance Information Provision Inventory Management Team Collaboration OHS Policy Development Safety Advice Risk Assessment Policy Review OHS Training Safety Inspections Unsafe Act Prevention Incident Investigation Report Preparation	281
Machine Learning Leadership Cross-Functional Collaboration Strategy Development ML/NLP Infrastructure Prototype Transformation ML System Design Algorithm Research Application Development Dataset Selection ML Testing Statistical Analysis R&D in ML/NLP Text Representation Data Pipeline Design Statistical Data Analysis Model Training Team Collaboration Research Reporting Algorithm Analysis	280
Other values (23)	6226

Length

Max length	587
Median length	197
Mean length	218.03039
Min length	72

Characters and Unicode

Total characters	1664662
Distinct characters	64
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Technical Support Troubleshooting Collaboration Documentation System Monitoring Software Deployment Training & Mentorship Industry Trends Field Visits
2nd row	Machine Learning Leadership Cross-Functional Collaboration Strategy Development ML/NLP Infrastructure Prototype Transformation ML System Design Algorithm Research Application Development Dataset Selection ML Testing Statistical Analysis R&D in ML/NLP Text Representation Data Pipeline Design Statistical Data Analysis Model Training Team Collaboration Research Reporting Algorithm Analysis
3rd row	Trade Marketing Executive Brand Visibility, Sales Targets Field Marketing, Campaigns, Product Distribution Brand Head Excel, KPIs Tracking
4th row	Apparel Sourcing Quality Garment Sourcing Reliable Partner Buyer/Vendor Communication
5th row	iOS Lifecycle Requirement Analysis Native Frameworks iOS Development API Integration Technical Communication UI Design Performance Optimization Feature Collaboration Bug Fixing Code Translation High-Performance Development Task Management Cross-Team Collaboration Code Quality

Common Values

Value	Count	Frequency (%)
Database Design & Development SQL Query Optimization Data Integrity & Security BI Solutions Development ETL Process Implementation Database Maintenance Backup & Restore Management Index Rebuilding & Performance Tuning SQL Server Clustering & High Availability SQL Server Replication High Availability Group Management Database Monitoring & Troubleshooting	284	3.7%
Full Stack Development Front-end: ReactJS, NextJS Backend: Python, Django API Design Server-Side Logic DRF (Django REST Framework) Database Management (PostgreSQL, MySQL) Version Control (Git) AWS (ECR, RDS, ECS, ALB, EC2, etc.) Linux, Docker, CI/CD, GitLab Terraform, Shell Scripting	283	3.7%
Design Creation CAD Drawings Design Optimization Team Collaboration Compliance Assurance Design Reviews Manufacturing Support Documentation	281	3.7%
Administrative Support Scheduling Filing & Documentation Communication Team Support Equipment Maintenance Information Provision Inventory Management Team Collaboration OHS Policy Development Safety Advice Risk Assessment Policy Review OHS Training Safety Inspections Unsafe Act Prevention Incident Investigation Report Preparation	281	3.7%
Machine Learning Leadership Cross-Functional Collaboration Strategy Development ML/NLP Infrastructure Prototype Transformation ML System Design Algorithm Research Application Development Dataset Selection ML Testing Statistical Analysis R&D in ML/NLP Text Representation Data Pipeline Design Statistical Data Analysis Model Training Team Collaboration Research Reporting Algorithm Analysis	280	3.7%
Mikrotik Router Configuration OLT Device Setup & Management Integration with Billing Software Network Monitoring Tools Integration Connectivity Troubleshooting Technical Support & Escalation Installation & Configuration GPON/EPON Expertise Cisco, OLT, MikroTik Knowledge	279	3.7%
Supervision Monitoring Construction Estimation Planning Material Management Project Coordination Quality Assurance Cost Control Inventory Operations Safety Error Escalation Miscellaneous Tasks	277	3.6%
Management Trainee Mechanical Systems Maintenance & Troubleshooting Performance Analysis Project Support Process Improvement Training & Development Administrative Support	277	3.6%
Apparel Sourcing Quality Garment Sourcing Reliable Partner Buyer/Vendor Communication	276	3.6%
Machine Learning Design Data Analysis Model Training AI Integration Innovation Cross-Functional Collaboration Model Deployment Documentation Analytical Skills Communication Team Collaboration	276	3.6%
Other values (18)	4841	63.4%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
	11385	6.0%
management	5457	2.9%
development	4132	2.2%
design	3876	2.0%
data	3809	2.0%
collaboration	3574	1.9%
support	3024	1.6%
analysis	2752	1.4%
documentation	2469	1.3%
monitoring	2456	1.3%
Other values (323)	147881	77.5%

Most occurring characters

Value	Count	Frequency (%)
e	141492	8.5%
n	132606	8.0%
i	128303	7.7%
a	118492	7.1%
t	115658	6.9%
	113410	6.8%
o	106615	6.4%
r	79793	4.8%
	72520	4.4%
s	58738	3.5%
Other values (54)	597035	35.9%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	1664662	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
e	141492	8.5%
n	132606	8.0%
i	128303	7.7%
a	118492	7.1%
t	115658	6.9%
	113410	6.8%
o	106615	6.4%
r	79793	4.8%
	72520	4.4%
s	58738	3.5%
Other values (54)	597035	35.9%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	1664662	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
e	141492	8.5%
n	132606	8.0%
i	128303	7.7%
a	118492	7.1%
t	115658	6.9%
	113410	6.8%
o	106615	6.4%
r	79793	4.8%
	72520	4.4%
s	58738	3.5%
Other values (54)	597035	35.9%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	1664662	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
e	141492	8.5%
n	132606	8.0%
i	128303	7.7%
a	118492	7.1%
t	115658	6.9%
	113410	6.8%
o	106615	6.4%
r	79793	4.8%
	72520	4.4%
s	58738	3.5%
Other values (54)	597035	35.9%

extra_curricular_activity_types
Text

Missing

Distinct	86
Distinct (%)	3.1%
Missing	4903
Missing (%)	64.2%
Memory size	59.8 KiB

Length

Max length	123
Median length	51
Mean length	26.225842
Min length	6

Characters and Unicode

Total characters	71649
Distinct characters	55
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	< 0.1%

Sample

1st row	['Professional Organization', 'Honor Society', 'Honor Society', 'Honor Society']
2nd row	['N/A']
3rd row	['Award', 'Competition', 'Competition']
4th row	['Competition', 'Competition', 'Membership']
5th row	['Scholarship']

Value	Count	Frequency (%)
volunteering	441	6.6%
course	401	6.0%
membership	344	5.1%
n/a	203	3.0%
competition	181	2.7%
club	175	2.6%
society	163	2.4%
data	163	2.4%
workshop	138	2.1%
learning	132	2.0%
Other values (139)	4351	65.0%

Most occurring characters

Value	Count	Frequency (%)
'	7924	11.1%
e	5741	8.0%
i	4639	6.5%
o	4254	5.9%
	3960	5.5%
r	3871	5.4%
n	3858	5.4%
t	3732	5.2%
a	2786	3.9%
[	2732	3.8%
Other values (45)	28152	39.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	71649	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
'	7924	11.1%
e	5741	8.0%
i	4639	6.5%
o	4254	5.9%
	3960	5.5%
r	3871	5.4%
n	3858	5.4%
t	3732	5.2%
a	2786	3.9%
[	2732	3.8%
Other values (45)	28152	39.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	71649	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
'	7924	11.1%
e	5741	8.0%
i	4639	6.5%
o	4254	5.9%
	3960	5.5%
r	3871	5.4%
n	3858	5.4%
t	3732	5.2%
a	2786	3.9%
[	2732	3.8%
Other values (45)	28152	39.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	71649	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
'	7924	11.1%
e	5741	8.0%
i	4639	6.5%
o	4254	5.9%
	3960	5.5%
r	3871	5.4%
n	3858	5.4%
t	3732	5.2%
a	2786	3.9%
[	2732	3.8%
Other values (45)	28152	39.3%

extra_curricular_organization_names
Text

Missing

Distinct	89
Distinct (%)	3.3%
Missing	4903
Missing (%)	64.2%
Memory size	59.8 KiB

Length

Max length	186
Median length	99
Mean length	31.755857
Min length	6

Characters and Unicode

Total characters	86757
Distinct characters	61
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	< 0.1%

Sample

1st row	['Ohio Society of CPAs', 'Beta Alpha Psi', 'Golden Key International Honour Society', 'Beta Gamma Sigma']
2nd row	['N/A']
3rd row	['N/A', 'N/A', 'Young Economist Innovation']
4th row	['The ECE Society, BIT Mesra, Ranchi', 'SGGSCC, University of Delhi', 'National Service Scheme']
5th row	['KSST']

Value	Count	Frequency (%)
n/a	934	8.9%
of	447	4.3%
society	277	2.6%
none	277	2.6%
university	200	1.9%
and	160	1.5%
learning	153	1.5%
association	130	1.2%
data	129	1.2%
google	119	1.1%
Other values (281)	7673	73.1%

Most occurring characters

Value	Count	Frequency (%)
	7767	9.0%
'	7347	8.5%
e	5359	6.2%
o	4991	5.8%
n	4946	5.7%
i	4735	5.5%
t	4197	4.8%
a	4126	4.8%
r	3168	3.7%
[	2732	3.1%
Other values (51)	37389	43.1%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	86757	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	7767	9.0%
'	7347	8.5%
e	5359	6.2%
o	4991	5.8%
n	4946	5.7%
i	4735	5.5%
t	4197	4.8%
a	4126	4.8%
r	3168	3.7%
[	2732	3.1%
Other values (51)	37389	43.1%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	86757	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	7767	9.0%
'	7347	8.5%
e	5359	6.2%
o	4991	5.8%
n	4946	5.7%
i	4735	5.5%
t	4197	4.8%
a	4126	4.8%
r	3168	3.7%
[	2732	3.1%
Other values (51)	37389	43.1%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	86757	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	7767	9.0%
'	7347	8.5%
e	5359	6.2%
o	4991	5.8%
n	4946	5.7%
i	4735	5.5%
t	4197	4.8%
a	4126	4.8%
r	3168	3.7%
[	2732	3.1%
Other values (51)	37389	43.1%

extra_curricular_organization_links
Categorical

High correlation Missing

Distinct	9
Distinct (%)	0.3%
Missing	4903
Missing (%)	64.2%
Memory size	59.8 KiB

[None]	1155
['N/A']	764
[None, None]	476
[None, None, None]	160
['N/A', 'N/A']	85
Other values (4)	92

Length

Max length	42
Median length	36
Mean length	9.1222548
Min length	6

Characters and Unicode

Total characters	24922
Distinct characters	11
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	< 0.1%

Sample

1st row	[None, None, None, None]
2nd row	['N/A']
3rd row	[None, None, None]
4th row	[None, None, None]
5th row	['N/A']

Common Values

Value	Count	Frequency (%)
[None]	1155	15.1%
['N/A']	764	10.0%
[None, None]	476	6.2%
[None, None, None]	160	2.1%
['N/A', 'N/A']	85	1.1%
[None, None, None, None]	47	0.6%
[None, None, None, None, None, None]	24	0.3%
[None, None, None, None, None, None, None]	20	0.3%
[None, None, None, None, None]	1	< 0.1%
(Missing)	4903	64.2%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
none	3064	76.6%
n/a	934	23.4%

Most occurring characters

Value	Count	Frequency (%)
N	3998	16.0%
o	3064	12.3%
n	3064	12.3%
e	3064	12.3%
[	2732	11.0%
]	2732	11.0%
'	1868	7.5%
,	1266	5.1%
	1266	5.1%
/	934	3.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	24922	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
N	3998	16.0%
o	3064	12.3%
n	3064	12.3%
e	3064	12.3%
[	2732	11.0%
]	2732	11.0%
'	1868	7.5%
,	1266	5.1%
	1266	5.1%
/	934	3.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	24922	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
N	3998	16.0%
o	3064	12.3%
n	3064	12.3%
e	3064	12.3%
[	2732	11.0%
]	2732	11.0%
'	1868	7.5%
,	1266	5.1%
	1266	5.1%
/	934	3.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	24922	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
N	3998	16.0%
o	3064	12.3%
n	3064	12.3%
e	3064	12.3%
[	2732	11.0%
]	2732	11.0%
'	1868	7.5%
,	1266	5.1%
	1266	5.1%
/	934	3.7%

role_positions
Text

Missing

Distinct	91
Distinct (%)	3.3%
Missing	4903
Missing (%)	64.2%
Memory size	59.8 KiB

Length

Max length	236
Median length	67
Mean length	30.379209
Min length	6

Characters and Unicode

Total characters	82996
Distinct characters	68
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	< 0.1%

Sample

1st row	[None, None, None, None]
2nd row	['N/A']
3rd row	['Silver Medal For Economics Junior Award', 'Finalist at business solution PAN India', 'Winner and Runner-Up for 2018 and 2019 respectively']
4th row	['Participant', 'Winner', 'Member']
5th row	['Scholar']

Value	Count	Frequency (%)
n/a	566	5.5%
data	422	4.1%
member	408	4.0%
python	369	3.6%
none	351	3.4%
participant	343	3.3%
with	277	2.7%
and	277	2.7%
learning	237	2.3%
science	204	2.0%
Other values (192)	6809	66.3%

Most occurring characters

Value	Count	Frequency (%)
	7531	9.1%
'	7272	8.8%
e	6197	7.5%
n	5205	6.3%
a	4843	5.8%
t	4546	5.5%
i	4348	5.2%
r	3953	4.8%
o	3788	4.6%
[	2732	3.3%
Other values (58)	32581	39.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	82996	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	7531	9.1%
'	7272	8.8%
e	6197	7.5%
n	5205	6.3%
a	4843	5.8%
t	4546	5.5%
i	4348	5.2%
r	3953	4.8%
o	3788	4.6%
[	2732	3.3%
Other values (58)	32581	39.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	82996	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	7531	9.1%
'	7272	8.8%
e	6197	7.5%
n	5205	6.3%
a	4843	5.8%
t	4546	5.5%
i	4348	5.2%
r	3953	4.8%
o	3788	4.6%
[	2732	3.3%
Other values (58)	32581	39.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	82996	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	7531	9.1%
'	7272	8.8%
e	6197	7.5%
n	5205	6.3%
a	4843	5.8%
t	4546	5.5%
i	4348	5.2%
r	3953	4.8%
o	3788	4.6%
[	2732	3.3%
Other values (58)	32581	39.3%

languages
Categorical

High correlation Missing

Distinct	18
Distinct (%)	3.2%
Missing	7066
Missing (%)	92.5%
Memory size	59.8 KiB

['English', 'Spanish']	131
['Spanish']	72
['Portuguese']	26
['English', 'Marathi', 'Hindi', 'Gujarati']	25
['English', 'French', 'Italian', 'Greek']	25
Other values (13)	290

Length

Max length	43
Median length	34
Mean length	24.936731
Min length	10

Characters and Unicode

Total characters	14189
Distinct characters	39
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	['Spanish']
2nd row	['English', 'Spanish']
3rd row	['English', 'Spanish']
4th row	['English', 'Hebrew']
5th row	['English', 'Hebrew']

Common Values

Value	Count	Frequency (%)
['English', 'Spanish']	131	1.7%
['Spanish']	72	0.9%
['Portuguese']	26	0.3%
['English', 'Marathi', 'Hindi', 'Gujarati']	25	0.3%
['English', 'French', 'Italian', 'Greek']	25	0.3%
['English', 'Swahili', 'Kalenjin']	25	0.3%
['Spanish', 'English', 'Portuguese']	24	0.3%
['English', 'Urdu', 'German']	24	0.3%
['English', 'Chinese Mandarin']	23	0.3%
['English', 'Arabic', 'Swedish']	23	0.3%
Other values (8)	171	2.2%
(Missing)	7066	92.5%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
english	449	34.5%
spanish	247	19.0%
french	67	5.2%
portuguese	50	3.8%
hindi	47	3.6%
mandarin	44	3.4%
arabic	44	3.4%
marathi	25	1.9%
gujarati	25	1.9%
italian	25	1.9%
Other values (12)	277	21.3%

Most occurring characters

Value	Count	Frequency (%)
'	2554	18.0%
i	1096	7.7%
n	1084	7.6%
h	881	6.2%
s	857	6.0%
a	756	5.3%
	731	5.2%
,	708	5.0%
l	590	4.2%
]	569	4.0%
Other values (29)	4363	30.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	14189	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
'	2554	18.0%
i	1096	7.7%
n	1084	7.6%
h	881	6.2%
s	857	6.0%
a	756	5.3%
	731	5.2%
,	708	5.0%
l	590	4.2%
]	569	4.0%
Other values (29)	4363	30.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	14189	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
'	2554	18.0%
i	1096	7.7%
n	1084	7.6%
h	881	6.2%
s	857	6.0%
a	756	5.3%
	731	5.2%
,	708	5.0%
l	590	4.2%
]	569	4.0%
Other values (29)	4363	30.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	14189	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
'	2554	18.0%
i	1096	7.7%
n	1084	7.6%
h	881	6.2%
s	857	6.0%
a	756	5.3%
	731	5.2%
,	708	5.0%
l	590	4.2%
]	569	4.0%
Other values (29)	4363	30.7%

proficiency_levels
Categorical

High correlation Missing

Distinct	22
Distinct (%)	3.9%
Missing	7066
Missing (%)	92.5%
Memory size	59.8 KiB

['Fluent', 'Fluent', 'Fluent']	69
['Bilingual', 'Bilingual']	43
['Fluent']	27
['fluent']	26
['N/A', 'N/A', 'N/A', 'N/A']	25
Other values (17)	379

Length

Max length	82
Median length	36
Mean length	30.801406
Min length	7

Characters and Unicode

Total characters	17526
Distinct characters	41
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	['N/A']
2nd row	['Native Speaker', 'Native Speaker']
3rd row	['Fluent', 'Decreased proficiency due to moderate use']
4th row	['Fluent', 'Native']
5th row	['Fluent', 'Native']

Common Values

Value	Count	Frequency (%)
['Fluent', 'Fluent', 'Fluent']	69	0.9%
['Bilingual', 'Bilingual']	43	0.6%
['Fluent']	27	0.4%
['fluent']	26	0.3%
['N/A', 'N/A', 'N/A', 'N/A']	25	0.3%
['Fluent', 'Fluent', 'Fluent', 'Fluent']	25	0.3%
['Fluent', 'Fluent', 'GCSE level']	24	0.3%
['Fluent - Full Knowledge', 'Knowledge, but taking classes to become more fluent']	24	0.3%
['Bilingual', 'Bilingual', 'Fluent reading/writing']	24	0.3%
['Advanced', 'Fluent']	24	0.3%
Other values (12)	258	3.4%
(Missing)	7066	92.5%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
fluent	566	28.6%
n/a	234	11.8%
bilingual	134	6.8%
native	103	5.2%
speaker	60	3.0%
knowledge	48	2.4%
bi-lingual	46	2.3%
to	44	2.2%
and	42	2.1%
proficiency	42	2.1%
Other values (29)	660	33.4%

Most occurring characters

Value	Count	Frequency (%)
'	2474	14.1%
e	1490	8.5%
	1410	8.0%
n	1164	6.6%
l	1116	6.4%
t	953	5.4%
u	855	4.9%
i	794	4.5%
,	774	4.4%
a	585	3.3%
Other values (31)	5911	33.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	17526	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
'	2474	14.1%
e	1490	8.5%
	1410	8.0%
n	1164	6.6%
l	1116	6.4%
t	953	5.4%
u	855	4.9%
i	794	4.5%
,	774	4.4%
a	585	3.3%
Other values (31)	5911	33.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	17526	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
'	2474	14.1%
e	1490	8.5%
	1410	8.0%
n	1164	6.6%
l	1116	6.4%
t	953	5.4%
u	855	4.9%
i	794	4.5%
,	774	4.4%
a	585	3.3%
Other values (31)	5911	33.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	17526	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
'	2474	14.1%
e	1490	8.5%
	1410	8.0%
n	1164	6.6%
l	1116	6.4%
t	953	5.4%
u	855	4.9%
i	794	4.5%
,	774	4.4%
a	585	3.3%
Other values (31)	5911	33.7%

certification_providers
Categorical

High correlation Missing

Distinct	49
Distinct (%)	3.1%
Missing	6048
Missing (%)	79.2%
Memory size	59.8 KiB

['N/A']	440
['AWS']	47
['N/A', 'N/A']	45
['Tableau']	45
[None]	44
Other values (44)	966

Length

Max length	273
Median length	76
Mean length	26.040958
Min length	6

Characters and Unicode

Total characters	41327
Distinct characters	60
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	['Ohio Notary Public']
2nd row	['Microsoft Certification']
3rd row	['AIT']
4th row	['Google Cloud']
5th row	['N/A']

Common Values

Value	Count	Frequency (%)
['N/A']	440	5.8%
['AWS']	47	0.6%
['N/A', 'N/A']	45	0.6%
['Tableau']	45	0.6%
[None]	44	0.6%
['PTA']	25	0.3%
['New York State']	25	0.3%
['IBM']	25	0.3%
['State of Maine']	25	0.3%
['Management Leadership Certification', 'Six Sigma Greenbelt certification']	24	0.3%
Other values (39)	842	11.0%
(Missing)	6048	79.2%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
n/a	530	11.1%
certified	106	2.2%
aws	92	1.9%
scrum	90	1.9%
of	84	1.8%
state	71	1.5%
certification	71	1.5%
udemy	69	1.4%
linkedin	69	1.4%
six	64	1.3%
Other values (141)	3518	73.8%

Most occurring characters

Value	Count	Frequency (%)
'	5200	12.6%
	3177	7.7%
e	2732	6.6%
a	1887	4.6%
n	1799	4.4%
i	1799	4.4%
o	1772	4.3%
t	1694	4.1%
r	1690	4.1%
[	1587	3.8%
Other values (50)	17990	43.5%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	41327	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
'	5200	12.6%
	3177	7.7%
e	2732	6.6%
a	1887	4.6%
n	1799	4.4%
i	1799	4.4%
o	1772	4.3%
t	1694	4.1%
r	1690	4.1%
[	1587	3.8%
Other values (50)	17990	43.5%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	41327	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
'	5200	12.6%
	3177	7.7%
e	2732	6.6%
a	1887	4.6%
n	1799	4.4%
i	1799	4.4%
o	1772	4.3%
t	1694	4.1%
r	1690	4.1%
[	1587	3.8%
Other values (50)	17990	43.5%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	41327	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
'	5200	12.6%
	3177	7.7%
e	2732	6.6%
a	1887	4.6%
n	1799	4.4%
i	1799	4.4%
o	1772	4.3%
t	1694	4.1%
r	1690	4.1%
[	1587	3.8%
Other values (50)	17990	43.5%

certification_skills
Categorical

High correlation Missing

Distinct	32
Distinct (%)	2.0%
Missing	6048
Missing (%)	79.2%
Memory size	59.8 KiB

[None]	666
[None, None]	222
[['Python']]	43
[None, None, None]	42
[['Python Programming'], ['Python Programming']]	25
Other values (27)	589

Length

Max length	272
Median length	166
Mean length	23.405167
Min length	4

Characters and Unicode

Total characters	37144
Distinct characters	59
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	[None]
2nd row	[None]
3rd row	[None]
4th row	[None]
5th row	[None]

Common Values

Value	Count	Frequency (%)
[None]	666	8.7%
[None, None]	222	2.9%
[['Python']]	43	0.6%
[None, None, None]	42	0.6%
[['Python Programming'], ['Python Programming']]	25	0.3%
[['Data Science']]	25	0.3%
[['Tableau Visualization']]	24	0.3%
[['HIPAA Compliance']]	24	0.3%
[[]]	24	0.3%
[['Selenium']]	24	0.3%
Other values (22)	468	6.1%
(Missing)	6048	79.2%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
none	1368	33.2%
python	249	6.0%
advanced	189	4.6%
intermediate	147	3.6%
data	91	2.2%
learning	90	2.2%
science	68	1.6%
sql	68	1.6%
and	67	1.6%
programming	50	1.2%
Other values (69)	1738	42.1%

Most occurring characters

Value	Count	Frequency (%)
e	3460	9.3%
'	3294	8.9%
n	3193	8.6%
[	2863	7.7%
]	2863	7.7%
	2538	6.8%
o	2255	6.1%
N	1543	4.2%
,	1498	4.0%
a	1463	3.9%
Other values (49)	12174	32.8%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	37144	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
e	3460	9.3%
'	3294	8.9%
n	3193	8.6%
[	2863	7.7%
]	2863	7.7%
	2538	6.8%
o	2255	6.1%
N	1543	4.2%
,	1498	4.0%
a	1463	3.9%
Other values (49)	12174	32.8%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	37144	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
e	3460	9.3%
'	3294	8.9%
n	3193	8.6%
[	2863	7.7%
]	2863	7.7%
	2538	6.8%
o	2255	6.1%
N	1543	4.2%
,	1498	4.0%
a	1463	3.9%
Other values (49)	12174	32.8%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	37144	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
e	3460	9.3%
'	3294	8.9%
n	3193	8.6%
[	2863	7.7%
]	2863	7.7%
	2538	6.8%
o	2255	6.1%
N	1543	4.2%
,	1498	4.0%
a	1463	3.9%
Other values (49)	12174	32.8%

online_links
Categorical

High correlation Missing

Distinct	7
Distinct (%)	0.4%
Missing	6048
Missing (%)	79.2%
Memory size	59.8 KiB

[None]	950
[None, None]	325
['N/A']	160
[None, None, None]	64
[None, None, None, None]	44
Other values (2)	44

Length

Max length	108
Median length	6
Mean length	10.097038
Min length	6

Characters and Unicode

Total characters	16024
Distinct characters	11
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	[None]
2nd row	[None]
3rd row	[None]
4th row	[None]
5th row	['N/A']

Common Values

Value	Count	Frequency (%)
[None]	950	12.4%
[None, None]	325	4.3%
['N/A']	160	2.1%
[None, None, None]	64	0.8%
[None, None, None, None]	44	0.6%
[None, None, None, None, None, None]	23	0.3%
[None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None]	21	0.3%
(Missing)	6048	79.2%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
none	2484	93.9%
n/a	160	6.1%

Most occurring characters

Value	Count	Frequency (%)
N	2644	16.5%
o	2484	15.5%
n	2484	15.5%
e	2484	15.5%
[	1587	9.9%
]	1587	9.9%
,	1057	6.6%
	1057	6.6%
'	320	2.0%
/	160	1.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	16024	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
N	2644	16.5%
o	2484	15.5%
n	2484	15.5%
e	2484	15.5%
[	1587	9.9%
]	1587	9.9%
,	1057	6.6%
	1057	6.6%
'	320	2.0%
/	160	1.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	16024	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
N	2644	16.5%
o	2484	15.5%
n	2484	15.5%
e	2484	15.5%
[	1587	9.9%
]	1587	9.9%
,	1057	6.6%
	1057	6.6%
'	320	2.0%
/	160	1.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	16024	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
N	2644	16.5%
o	2484	15.5%
n	2484	15.5%
e	2484	15.5%
[	1587	9.9%
]	1587	9.9%
,	1057	6.6%
	1057	6.6%
'	320	2.0%
/	160	1.0%

issue_dates
Categorical

High correlation Missing

Distinct	30
Distinct (%)	1.9%
Missing	6048
Missing (%)	79.2%
Memory size	59.8 KiB

['N/A']	508
[None]	221
[None, None]	154
['N/A', 'N/A']	105
['N/A', 'N/A', 'N/A']	44
Other values (25)	555

Length

Max length	108
Median length	36
Mean length	12.31695
Min length	6

Characters and Unicode

Total characters	19547
Distinct characters	38
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	[None]
2nd row	['May 2013']
3rd row	[None]
4th row	[None]
5th row	['N/A']

Common Values

Value	Count	Frequency (%)
['N/A']	508	6.7%
[None]	221	2.9%
[None, None]	154	2.0%
['N/A', 'N/A']	105	1.4%
['N/A', 'N/A', 'N/A']	44	0.6%
['03/04/2015']	25	0.3%
['1981']	25	0.3%
['2011']	25	0.3%
['2002']	24	0.3%
['Feb 2017', 'Mar 2017']	24	0.3%
Other values (20)	432	5.7%
(Missing)	6048	79.2%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
none	1105	37.9%
n/a	869	29.8%
2013	110	3.8%
2021	69	2.4%
2015	64	2.2%
2011	48	1.6%
2017	48	1.6%
feb	47	1.6%
2014	46	1.6%
dec	45	1.5%
Other values (18)	462	15.9%

Most occurring characters

Value	Count	Frequency (%)
'	3056	15.6%
N	2016	10.3%
[	1587	8.1%
]	1587	8.1%
	1326	6.8%
e	1258	6.4%
o	1170	6.0%
n	1105	5.7%
,	1057	5.4%
/	939	4.8%
Other values (28)	4446	22.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	19547	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
'	3056	15.6%
N	2016	10.3%
[	1587	8.1%
]	1587	8.1%
	1326	6.8%
e	1258	6.4%
o	1170	6.0%
n	1105	5.7%
,	1057	5.4%
/	939	4.8%
Other values (28)	4446	22.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	19547	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
'	3056	15.6%
N	2016	10.3%
[	1587	8.1%
]	1587	8.1%
	1326	6.8%
e	1258	6.4%
o	1170	6.0%
n	1105	5.7%
,	1057	5.4%
/	939	4.8%
Other values (28)	4446	22.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	19547	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
'	3056	15.6%
N	2016	10.3%
[	1587	8.1%
]	1587	8.1%
	1326	6.8%
e	1258	6.4%
o	1170	6.0%
n	1105	5.7%
,	1057	5.4%
/	939	4.8%
Other values (28)	4446	22.7%

expiry_dates
Categorical

High correlation Missing

Distinct	11
Distinct (%)	0.7%
Missing	6048
Missing (%)	79.2%
Memory size	59.8 KiB

[None]	814
[None, None]	301
['N/A']	227
[None, None, None]	64
[None, None, None, None]	44
Other values (6)	137

Length

Max length	108
Median length	6
Mean length	10.647763
Min length	6

Characters and Unicode

Total characters	16898
Distinct characters	26
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	['February 15, 2021']
2nd row	[None]
3rd row	[None]
4th row	[None]
5th row	['N/A']

Common Values

Value	Count	Frequency (%)
[None]	814	10.7%
[None, None]	301	3.9%
['N/A']	227	3.0%
[None, None, None]	64	0.8%
[None, None, None, None]	44	0.6%
[None, 'Mar 2018']	24	0.3%
['2015']	24	0.3%
['February 15, 2021']	23	0.3%
['May 2022', None, None, None, None, None]	23	0.3%
['Dec 2016']	22	0.3%
(Missing)	6048	79.2%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
none	2301	83.4%
n/a	227	8.2%
mar	24	0.9%
2018	24	0.9%
2015	24	0.9%
february	23	0.8%
15	23	0.8%
2021	23	0.8%
may	23	0.8%
2022	23	0.8%
Other values (2)	44	1.6%

Most occurring characters

Value	Count	Frequency (%)
N	2528	15.0%
e	2346	13.9%
o	2301	13.6%
n	2301	13.6%
[	1587	9.4%
]	1587	9.4%
	1172	6.9%
,	1080	6.4%
'	686	4.1%
/	227	1.3%
Other values (16)	1083	6.4%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	16898	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
N	2528	15.0%
e	2346	13.9%
o	2301	13.6%
n	2301	13.6%
[	1587	9.4%
]	1587	9.4%
	1172	6.9%
,	1080	6.4%
'	686	4.1%
/	227	1.3%
Other values (16)	1083	6.4%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	16898	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
N	2528	15.0%
e	2346	13.9%
o	2301	13.6%
n	2301	13.6%
[	1587	9.4%
]	1587	9.4%
	1172	6.9%
,	1080	6.4%
'	686	4.1%
/	227	1.3%
Other values (16)	1083	6.4%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	16898	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
N	2528	15.0%
e	2346	13.9%
o	2301	13.6%
n	2301	13.6%
[	1587	9.4%
]	1587	9.4%
	1172	6.9%
,	1080	6.4%
'	686	4.1%
/	227	1.3%
Other values (16)	1083	6.4%

job_position_name
Categorical

High correlation Uniform

Distinct	28
Distinct (%)	0.4%
Missing	0
Missing (%)	0.0%
Memory size	59.8 KiB

Database Administrator (DBA)	284
Full Stack Developer (Python,React js)	283
Mechanical Designer	281
Asst. Manager/ Manger (Administrative)	281
Machine Learning (ML) Engineer	280
Other values (23)	6226

Length

Max length	87
Median length	38
Mean length	29.481205
Min length	10

Characters and Unicode

Total characters	225089
Distinct characters	53
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Senior Software Engineer
2nd row	Machine Learning (ML) Engineer
3rd row	Executive/ Senior Executive- Trade Marketing, Hygiene Products
4th row	Business Development Executive
5th row	Senior iOS Engineer

Common Values

Value	Count	Frequency (%)
Database Administrator (DBA)	284	3.7%
Full Stack Developer (Python,React js)	283	3.7%
Mechanical Designer	281	3.7%
Asst. Manager/ Manger (Administrative)	281	3.7%
Machine Learning (ML) Engineer	280	3.7%
Network Support Engineer	279	3.7%
Site Engineer	277	3.6%
Management Trainee - Mechanical	277	3.6%
Business Development Executive	276	3.6%
AI Engineer	276	3.6%
Other values (18)	4841	63.4%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
engineer	3004	10.0%
	2413	8.1%
executive	1896	6.3%
mechanical	827	2.8%
senior	822	2.7%
administrator	552	1.8%
manager	552	1.8%
management	548	1.8%
ai	546	1.8%
data	544	1.8%
Other values (61)	18213	60.9%

Most occurring characters

Value	Count	Frequency (%)
e	28898	12.8%
	22282	9.9%
n	19096	8.5%
i	16094	7.2%
r	14710	6.5%
a	12874	5.7%
t	12018	5.3%
c	7356	3.3%
g	6838	3.0%
o	6799	3.0%
Other values (43)	78124	34.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	225089	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
e	28898	12.8%
	22282	9.9%
n	19096	8.5%
i	16094	7.2%
r	14710	6.5%
a	12874	5.7%
t	12018	5.3%
c	7356	3.3%
g	6838	3.0%
o	6799	3.0%
Other values (43)	78124	34.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	225089	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
e	28898	12.8%
	22282	9.9%
n	19096	8.5%
i	16094	7.2%
r	14710	6.5%
a	12874	5.7%
t	12018	5.3%
c	7356	3.3%
g	6838	3.0%
o	6799	3.0%
Other values (43)	78124	34.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	225089	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
e	28898	12.8%
	22282	9.9%
n	19096	8.5%
i	16094	7.2%
r	14710	6.5%
a	12874	5.7%
t	12018	5.3%
c	7356	3.3%
g	6838	3.0%
o	6799	3.0%
Other values (43)	78124	34.7%

educationaL_requirements
Categorical

High correlation

Distinct	20
Distinct (%)	0.3%
Missing	0
Missing (%)	0.0%
Memory size	59.8 KiB

Bachelor/Honors	1634
Bachelor of Science (BSc) in Computer Science	558
Bachelor of Science (BSc) in Computer Science & Engineering	540
Bachelor of Science (BSc)	539
Bachelor’s degree in Mechanical Engineering from a reputed institute.	281
Other values (15)	4083

Length

Max length	127
Median length	75
Mean length	51.716437
Min length	15

Characters and Unicode

Total characters	394855
Distinct characters	41
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	B.Sc in Computer Science & Engineering from a reputed university.
2nd row	M.Sc in Computer Science & Engineering or in any relevant discipline from a reputed University
3rd row	Master of Business Administration (MBA)
4th row	Bachelor/Honors
5th row	Bachelor of Science (BSc) in Computer Science

Common Values

Value	Count	Frequency (%)
Bachelor/Honors	1634	21.4%
Bachelor of Science (BSc) in Computer Science	558	7.3%
Bachelor of Science (BSc) in Computer Science & Engineering	540	7.1%
Bachelor of Science (BSc)	539	7.1%
Bachelor’s degree in Mechanical Engineering from a reputed institute.	281	3.7%
M.Sc in Computer Science & Engineering or in any relevant discipline from a reputed University	280	3.7%
Diploma, Bachelor/Honors	279	3.7%
Fresh graduates with a Bachelor’s degree in Mechanical Engineering or a related field.	277	3.6%
Bachelor of Science (BSc) in Civil Engineering	277	3.6%
Bachelors or Masters degree in Computer Science, Engineering, or a related field.	276	3.6%
Other values (10)	2694	35.3%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
in	5459	9.9%
science	4110	7.5%
of	4054	7.4%
engineering	2743	5.0%
bachelor	2454	4.5%
bsc	2183	4.0%
a	1934	3.5%
computer	1927	3.5%
bachelor/honors	1913	3.5%
from	1635	3.0%
Other values (45)	26587	48.3%

Most occurring characters

Value	Count	Frequency (%)
	47101	11.9%
e	41213	10.4%
n	33191	8.4%
i	30523	7.7%
r	24562	6.2%
c	22396	5.7%
o	22056	5.6%
a	19827	5.0%
s	14854	3.8%
B	13807	3.5%
Other values (31)	125325	31.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	394855	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	47101	11.9%
e	41213	10.4%
n	33191	8.4%
i	30523	7.7%
r	24562	6.2%
c	22396	5.7%
o	22056	5.6%
a	19827	5.0%
s	14854	3.8%
B	13807	3.5%
Other values (31)	125325	31.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	394855	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	47101	11.9%
e	41213	10.4%
n	33191	8.4%
i	30523	7.7%
r	24562	6.2%
c	22396	5.7%
o	22056	5.6%
a	19827	5.0%
s	14854	3.8%
B	13807	3.5%
Other values (31)	125325	31.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	394855	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	47101	11.9%
e	41213	10.4%
n	33191	8.4%
i	30523	7.7%
r	24562	6.2%
c	22396	5.7%
o	22056	5.6%
a	19827	5.0%
s	14854	3.8%
B	13807	3.5%
Other values (31)	125325	31.7%

experiencere_requirement
Categorical

High correlation Missing

Distinct	17
Distinct (%)	0.3%
Missing	1086
Missing (%)	14.2%
Memory size	59.8 KiB

At least 5 years	832
At least 1 year	821
At least 3 years	816
1 to 3 years	548
3 to 7 years	283
Other values (12)	3249

Length

Max length	18
Median length	17
Mean length	14.2153
Min length	12

Characters and Unicode

Total characters	93096
Distinct characters	21
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	At least 1 year
2nd row	At least 5 year(s)
3rd row	At least 3 years
4th row	1 to 3 years
5th row	At least 4 years

Common Values

Value	Count	Frequency (%)
At least 5 years	832	10.9%
At least 1 year	821	10.8%
At least 3 years	816	10.7%
1 to 3 years	548	7.2%
3 to 7 years	283	3.7%
At least 5 year(s)	280	3.7%
1 to 2 years	277	3.6%
At least 2 years	275	3.6%
At least 4 years	274	3.6%
5 to 10 years	272	3.6%
Other values (7)	1871	24.5%
(Missing)	1086	14.2%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
years	5448	20.8%
at	3559	13.6%
least	3559	13.6%
to	2990	11.4%
5	2726	10.4%
3	1912	7.3%
1	1646	6.3%
2	1089	4.2%
year	821	3.1%
4	810	3.1%
Other values (6)	1636	6.2%

Most occurring characters

Value	Count	Frequency (%)
	19647	21.1%
e	10108	10.9%
a	10108	10.9%
t	10108	10.9%
s	9287	10.0%
y	6549	7.0%
r	6549	7.0%
A	3559	3.8%
l	3559	3.8%
o	2990	3.2%
Other values (11)	10632	11.4%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	93096	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	19647	21.1%
e	10108	10.9%
a	10108	10.9%
t	10108	10.9%
s	9287	10.0%
y	6549	7.0%
r	6549	7.0%
A	3559	3.8%
l	3559	3.8%
o	2990	3.2%
Other values (11)	10632	11.4%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	93096	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	19647	21.1%
e	10108	10.9%
a	10108	10.9%
t	10108	10.9%
s	9287	10.0%
y	6549	7.0%
r	6549	7.0%
A	3559	3.8%
l	3559	3.8%
o	2990	3.2%
Other values (11)	10632	11.4%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	93096	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	19647	21.1%
e	10108	10.9%
a	10108	10.9%
t	10108	10.9%
s	9287	10.0%
y	6549	7.0%
r	6549	7.0%
A	3559	3.8%
l	3559	3.8%
o	2990	3.2%
Other values (11)	10632	11.4%

age_requirement
Categorical

High correlation Missing

Distinct	14
Distinct (%)	0.3%
Missing	3264
Missing (%)	42.8%
Memory size	59.8 KiB

Age 25 to 40 years	824
Age at least 24 years	281
Age at least 28 years	281
Age 25 to 35 years	279
Age at least 22 years	277
Other values (9)	2429

Length

Max length	21
Median length	18
Mean length	19.00183
Min length	18

Characters and Unicode

Total characters	83057
Distinct characters	19
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Age 22 to 30 years
2nd row	Age 25 to 40 years
3rd row	Age 22 to 30 years
4th row	Age at least 24 years
5th row	Age at least 28 years

Common Values

Value	Count	Frequency (%)
Age 25 to 40 years	824	10.8%
Age at least 24 years	281	3.7%
Age at least 28 years	281	3.7%
Age 25 to 35 years	279	3.7%
Age at least 22 years	277	3.6%
Age 25 to 30 years	277	3.6%
Age 22 to 30 years	276	3.6%
Age 25 to 32 years	275	3.6%
Age 30 to 40 years	272	3.6%
Age 20 to 35 years	270	3.5%
Other values (4)	1059	13.9%
(Missing)	3264	42.8%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
age	4371	20.0%
years	4371	20.0%
to	2736	12.5%
25	1655	7.6%
at	1635	7.5%
40	1361	6.2%
30	1358	6.2%
least	1109	5.1%
22	553	2.5%
35	549	2.5%
Other values (7)	2157	9.9%

Most occurring characters

Value	Count	Frequency (%)
	17484	21.1%
e	9851	11.9%
a	7115	8.6%
s	6006	7.2%
t	6006	7.2%
A	4371	5.3%
r	4371	5.3%
g	4371	5.3%
y	4371	5.3%
2	4129	5.0%
Other values (9)	14982	18.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	83057	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	17484	21.1%
e	9851	11.9%
a	7115	8.6%
s	6006	7.2%
t	6006	7.2%
A	4371	5.3%
r	4371	5.3%
g	4371	5.3%
y	4371	5.3%
2	4129	5.0%
Other values (9)	14982	18.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	83057	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	17484	21.1%
e	9851	11.9%
a	7115	8.6%
s	6006	7.2%
t	6006	7.2%
A	4371	5.3%
r	4371	5.3%
g	4371	5.3%
y	4371	5.3%
2	4129	5.0%
Other values (9)	14982	18.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	83057	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	17484	21.1%
e	9851	11.9%
a	7115	8.6%
s	6006	7.2%
t	6006	7.2%
A	4371	5.3%
r	4371	5.3%
g	4371	5.3%
y	4371	5.3%
2	4129	5.0%
Other values (9)	14982	18.0%

responsibilities.1
Categorical

High correlation Uniform

Distinct	28
Distinct (%)	0.4%
Missing	0
Missing (%)	0.0%
Memory size	59.8 KiB

Database Design & Development SQL Query Optimization Data Integrity & Security BI Solutions Development ETL Process Implementation Database Maintenance Backup & Restore Management Index Rebuilding & Performance Tuning SQL Server Clustering & High Availability SQL Server Replication High Availability Group Management Database Monitoring & Troubleshooting	284
Full Stack Development Front-end: ReactJS, NextJS Backend: Python, Django API Design Server-Side Logic DRF (Django REST Framework) Database Management (PostgreSQL, MySQL) Version Control (Git) AWS (ECR, RDS, ECS, ALB, EC2, etc.) Linux, Docker, CI/CD, GitLab Terraform, Shell Scripting	283
Design Creation CAD Drawings Design Optimization Team Collaboration Compliance Assurance Design Reviews Manufacturing Support Documentation	281
Administrative Support Scheduling Filing & Documentation Communication Team Support Equipment Maintenance Information Provision Inventory Management Team Collaboration OHS Policy Development Safety Advice Risk Assessment Policy Review OHS Training Safety Inspections Unsafe Act Prevention Incident Investigation Report Preparation	281
Machine Learning Leadership Cross-Functional Collaboration Strategy Development ML/NLP Infrastructure Prototype Transformation ML System Design Algorithm Research Application Development Dataset Selection ML Testing Statistical Analysis R&D in ML/NLP Text Representation Data Pipeline Design Statistical Data Analysis Model Training Team Collaboration Research Reporting Algorithm Analysis	280
Other values (23)	6226

Length

Max length	587
Median length	197
Mean length	218.03039
Min length	72

Characters and Unicode

Total characters	1664662
Distinct characters	64
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Technical Support Troubleshooting Collaboration Documentation System Monitoring Software Deployment Training & Mentorship Industry Trends Field Visits
2nd row	Machine Learning Leadership Cross-Functional Collaboration Strategy Development ML/NLP Infrastructure Prototype Transformation ML System Design Algorithm Research Application Development Dataset Selection ML Testing Statistical Analysis R&D in ML/NLP Text Representation Data Pipeline Design Statistical Data Analysis Model Training Team Collaboration Research Reporting Algorithm Analysis
3rd row	Trade Marketing Executive Brand Visibility, Sales Targets Field Marketing, Campaigns, Product Distribution Brand Head Excel, KPIs Tracking
4th row	Apparel Sourcing Quality Garment Sourcing Reliable Partner Buyer/Vendor Communication
5th row	iOS Lifecycle Requirement Analysis Native Frameworks iOS Development API Integration Technical Communication UI Design Performance Optimization Feature Collaboration Bug Fixing Code Translation High-Performance Development Task Management Cross-Team Collaboration Code Quality

Common Values

Value	Count	Frequency (%)
Database Design & Development SQL Query Optimization Data Integrity & Security BI Solutions Development ETL Process Implementation Database Maintenance Backup & Restore Management Index Rebuilding & Performance Tuning SQL Server Clustering & High Availability SQL Server Replication High Availability Group Management Database Monitoring & Troubleshooting	284	3.7%
Full Stack Development Front-end: ReactJS, NextJS Backend: Python, Django API Design Server-Side Logic DRF (Django REST Framework) Database Management (PostgreSQL, MySQL) Version Control (Git) AWS (ECR, RDS, ECS, ALB, EC2, etc.) Linux, Docker, CI/CD, GitLab Terraform, Shell Scripting	283	3.7%
Design Creation CAD Drawings Design Optimization Team Collaboration Compliance Assurance Design Reviews Manufacturing Support Documentation	281	3.7%
Administrative Support Scheduling Filing & Documentation Communication Team Support Equipment Maintenance Information Provision Inventory Management Team Collaboration OHS Policy Development Safety Advice Risk Assessment Policy Review OHS Training Safety Inspections Unsafe Act Prevention Incident Investigation Report Preparation	281	3.7%
Machine Learning Leadership Cross-Functional Collaboration Strategy Development ML/NLP Infrastructure Prototype Transformation ML System Design Algorithm Research Application Development Dataset Selection ML Testing Statistical Analysis R&D in ML/NLP Text Representation Data Pipeline Design Statistical Data Analysis Model Training Team Collaboration Research Reporting Algorithm Analysis	280	3.7%
Mikrotik Router Configuration OLT Device Setup & Management Integration with Billing Software Network Monitoring Tools Integration Connectivity Troubleshooting Technical Support & Escalation Installation & Configuration GPON/EPON Expertise Cisco, OLT, MikroTik Knowledge	279	3.7%
Supervision Monitoring Construction Estimation Planning Material Management Project Coordination Quality Assurance Cost Control Inventory Operations Safety Error Escalation Miscellaneous Tasks	277	3.6%
Management Trainee Mechanical Systems Maintenance & Troubleshooting Performance Analysis Project Support Process Improvement Training & Development Administrative Support	277	3.6%
Apparel Sourcing Quality Garment Sourcing Reliable Partner Buyer/Vendor Communication	276	3.6%
Machine Learning Design Data Analysis Model Training AI Integration Innovation Cross-Functional Collaboration Model Deployment Documentation Analytical Skills Communication Team Collaboration	276	3.6%
Other values (18)	4841	63.4%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
	11385	6.0%
management	5457	2.9%
development	4132	2.2%
design	3876	2.0%
data	3809	2.0%
collaboration	3574	1.9%
support	3024	1.6%
analysis	2752	1.4%
documentation	2469	1.3%
monitoring	2456	1.3%
Other values (323)	147881	77.5%

Most occurring characters

Value	Count	Frequency (%)
e	141492	8.5%
n	132606	8.0%
i	128303	7.7%
a	118492	7.1%
t	115658	6.9%
	113410	6.8%
o	106615	6.4%
r	79793	4.8%
	72520	4.4%
s	58738	3.5%
Other values (54)	597035	35.9%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	1664662	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
e	141492	8.5%
n	132606	8.0%
i	128303	7.7%
a	118492	7.1%
t	115658	6.9%
	113410	6.8%
o	106615	6.4%
r	79793	4.8%
	72520	4.4%
s	58738	3.5%
Other values (54)	597035	35.9%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	1664662	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
e	141492	8.5%
n	132606	8.0%
i	128303	7.7%
a	118492	7.1%
t	115658	6.9%
	113410	6.8%
o	106615	6.4%
r	79793	4.8%
	72520	4.4%
s	58738	3.5%
Other values (54)	597035	35.9%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	1664662	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
e	141492	8.5%
n	132606	8.0%
i	128303	7.7%
a	118492	7.1%
t	115658	6.9%
	113410	6.8%
o	106615	6.4%
r	79793	4.8%
	72520	4.4%
s	58738	3.5%
Other values (54)	597035	35.9%

skills_required
Categorical

High correlation Missing Uniform

Distinct	23
Distinct (%)	0.4%
Missing	1371
Missing (%)	18.0%
Memory size	59.8 KiB

ASP.NET MVC Strong understanding of database design Database Administrator (DBA) Database management Elasticsearch MongoDB MySQL database NoSQL database REDIS	284
AutoCAD Solidworks	281
•Administration •Health Safety and Environment •Safety and Security Management	281
CCNA (Cisco Certified Network Associate) GPON Hardware & Networking IIG ISP IT Enabled services OLT and ONU	279
Computer skill Good communication skills Mechanical Engineering Quick learner and hard working	277
Other values (18)	4862

Length

Max length	163
Median length	63
Mean length	71.475894
Min length	11

Characters and Unicode

Total characters	447725
Distinct characters	58
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Brand Promotion Campaign Management Field Supervision Merchandising promotional activities Trade Marketing
2nd row	Fast typing skill IELTSInternet browsing & online work ability.
3rd row	iOS iOS App Developer iOS Application Development iOS Development Mobile apps Developer (iOS) Native IOS Swift (iOS) Swift UI
4th row	Python R or Java TensorFlow PyTorch Scikit-learn.
5th row	iOS iOS App Developer iOS Application Development iOS Development Mobile apps Developer (iOS) Native IOS Swift (iOS) Swift UI

Common Values

Value	Count	Frequency (%)
ASP.NET MVC Strong understanding of database design Database Administrator (DBA) Database management Elasticsearch MongoDB MySQL database NoSQL database REDIS	284	3.7%
AutoCAD Solidworks	281	3.7%
•Administration •Health Safety and Environment •Safety and Security Management	281	3.7%
CCNA (Cisco Certified Network Associate) GPON Hardware & Networking IIG ISP IT Enabled services OLT and ONU	279	3.7%
Computer skill Good communication skills Mechanical Engineering Quick learner and hard working	277	3.6%
AutoCAD Communication and negotiation skills Internet MS Office	277	3.6%
Fast typing skill IELTSInternet browsing & online work ability.	276	3.6%
Python R or Java TensorFlow PyTorch Scikit-learn.	276	3.6%
Brand Promotion Campaign Management Field Supervision Merchandising promotional activities Trade Marketing	275	3.6%
Business Analysis Effective communication skills Java REST API Design Soft Skills Software Development	275	3.6%
Other values (13)	3483	45.6%
(Missing)	1371	18.0%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
and	2727	4.5%
ios	1918	3.1%
database	1420	2.3%
management	1374	2.2%
skills	1104	1.8%
marketing	1067	1.7%
communication	829	1.4%
autocad	828	1.4%
	823	1.3%
development	823	1.3%
Other values (148)	48232	78.9%

Most occurring characters

Value	Count	Frequency (%)
	33881	7.6%
e	33808	7.6%
a	31721	7.1%
n	30063	6.7%
i	29838	6.7%
o	24821	5.5%
t	24392	5.4%
	21269	4.8%
r	19618	4.4%
s	15115	3.4%
Other values (48)	183199	40.9%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	447725	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	33881	7.6%
e	33808	7.6%
a	31721	7.1%
n	30063	6.7%
i	29838	6.7%
o	24821	5.5%
t	24392	5.4%
	21269	4.8%
r	19618	4.4%
s	15115	3.4%
Other values (48)	183199	40.9%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	447725	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	33881	7.6%
e	33808	7.6%
a	31721	7.1%
n	30063	6.7%
i	29838	6.7%
o	24821	5.5%
t	24392	5.4%
	21269	4.8%
r	19618	4.4%
s	15115	3.4%
Other values (48)	183199	40.9%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	447725	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	33881	7.6%
e	33808	7.6%
a	31721	7.1%
n	30063	6.7%
i	29838	6.7%
o	24821	5.5%
t	24392	5.4%
	21269	4.8%
r	19618	4.4%
s	15115	3.4%
Other values (48)	183199	40.9%

matched_score
Real number (ℝ)

Distinct	214
Distinct (%)	2.8%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.66066707

Minimum	0
Maximum	0.97
Zeros	1
Zeros (%)	< 0.1%
Negative	0
Negative (%)	0.0%
Memory size	59.8 KiB

Quantile statistics

Minimum	0
5-th percentile	0.35
Q1	0.57666667
median	0.68333333
Q3	0.79333333
95-th percentile	0.85
Maximum	0.97
Range	0.97
Interquartile range (IQR)	0.21666667

Descriptive statistics

Standard deviation	0.16740477
Coefficient of variation (CV)	0.25338748
Kurtosis	-0.17073382
Mean	0.66066707
Median Absolute Deviation (MAD)	0.11
Skewness	-0.79900472
Sum	5044.1931
Variance	0.028024356
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0.85	1186	15.5%
0.65	1066	14.0%
0.716666667	513	6.7%
0.783333333	467	6.1%
0.683333333	456	6.0%
0.35	404	5.3%
0.75	353	4.6%
0.816666667	333	4.4%
0.45	187	2.4%
0.55	169	2.2%
Other values (204)	2501	32.8%

Value	Count	Frequency (%)
0	1	< 0.1%
0.04	1	< 0.1%
0.05	2	< 0.1%
0.083333333	2	< 0.1%
0.09	1	< 0.1%
0.1	1	< 0.1%
0.116666667	2	< 0.1%
0.133333333	3	< 0.1%
0.14	1	< 0.1%
0.146666667	1	< 0.1%

Value	Count	Frequency (%)
0.97	1	< 0.1%
0.95	6	0.1%
0.943333333	2	< 0.1%
0.94	2	< 0.1%
0.936666667	7	0.1%
0.93	1	< 0.1%
0.926666667	6	0.1%
0.923333333	1	< 0.1%
0.92	2	< 0.1%
0.916666667	12	0.2%

Interactions

Correlations

	address	age_requirement	certification_providers	certification_skills	company_urls	educationaL_requirements	experiencere_requirement	expiry_dates	extra_curricular_organization_links	issue_dates	languages	matched_score	online_links	proficiency_levels	responsibilities	responsibilities.1	result_types	skills_required	job_position_name
address	1.000	0.000	1.000	0.992	0.987	0.000	0.000	0.984	0.988	0.984	1.000	0.217	0.984	1.000	0.000	0.000	0.986	0.000	0.000
age_requirement	0.000	1.000	0.000	0.000	0.000	0.920	0.894	0.000	0.000	0.000	0.000	0.211	0.000	0.000	1.000	1.000	0.000	1.000	1.000
certification_providers	1.000	0.000	1.000	0.844	0.694	0.000	0.000	0.960	0.809	0.895	0.997	0.212	0.925	1.000	0.000	0.000	0.836	0.000	0.000
certification_skills	0.992	0.000	0.844	1.000	0.569	0.000	0.000	0.867	0.620	0.714	0.993	0.142	0.959	0.990	0.000	0.000	0.735	0.000	0.000
company_urls	0.987	0.000	0.694	0.569	1.000	0.000	0.000	0.411	0.508	0.675	0.836	0.102	0.446	0.897	0.000	0.000	0.360	0.000	0.000
educationaL_requirements	0.000	0.920	0.000	0.000	0.000	1.000	0.844	0.000	0.000	0.000	0.000	0.192	0.000	0.000	0.999	0.999	0.000	1.000	0.999
experiencere_requirement	0.000	0.894	0.000	0.000	0.000	0.844	1.000	0.000	0.000	0.000	0.000	0.185	0.000	0.000	0.999	0.999	0.000	0.999	0.999
expiry_dates	0.984	0.000	0.960	0.867	0.411	0.000	0.000	1.000	0.654	0.919	0.993	0.096	0.937	0.990	0.000	0.000	0.590	0.000	0.000
extra_curricular_organization_links	0.988	0.000	0.809	0.620	0.508	0.000	0.000	0.654	1.000	0.718	0.989	0.041	0.551	0.986	0.000	0.000	0.421	0.000	0.000
issue_dates	0.984	0.000	0.895	0.714	0.675	0.000	0.000	0.919	0.718	1.000	0.931	0.152	0.928	0.993	0.000	0.000	0.720	0.000	0.000
languages	1.000	0.000	0.997	0.993	0.836	0.000	0.000	0.993	0.989	0.931	1.000	0.211	0.990	0.916	0.000	0.000	0.850	0.000	0.000
matched_score	0.217	0.211	0.212	0.142	0.102	0.192	0.185	0.096	0.041	0.152	0.211	1.000	0.119	0.214	0.225	0.225	0.104	0.227	0.225
online_links	0.984	0.000	0.925	0.959	0.446	0.000	0.000	0.937	0.551	0.928	0.990	0.119	1.000	0.987	0.000	0.000	0.493	0.000	0.000
proficiency_levels	1.000	0.000	1.000	0.990	0.897	0.000	0.000	0.990	0.986	0.993	0.916	0.214	0.987	1.000	0.000	0.000	0.900	0.000	0.000
responsibilities	0.000	1.000	0.000	0.000	0.000	0.999	0.999	0.000	0.000	0.000	0.000	0.225	0.000	0.000	1.000	1.000	0.000	1.000	1.000
responsibilities.1	0.000	1.000	0.000	0.000	0.000	0.999	0.999	0.000	0.000	0.000	0.000	0.225	0.000	0.000	1.000	1.000	0.000	1.000	1.000
result_types	0.986	0.000	0.836	0.735	0.360	0.000	0.000	0.590	0.421	0.720	0.850	0.104	0.493	0.900	0.000	0.000	1.000	0.000	0.000
skills_required	0.000	1.000	0.000	0.000	0.000	1.000	0.999	0.000	0.000	0.000	0.000	0.227	0.000	0.000	1.000	1.000	0.000	1.000	1.000
job_position_name	0.000	1.000	0.000	0.000	0.000	0.999	0.999	0.000	0.000	0.000	0.000	0.225	0.000	0.000	1.000	1.000	0.000	1.000	1.000

Missing values

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

	address	career_objective	skills	educational_institution_name	degree_names	passing_years	educational_results	result_types	major_field_of_studies	professional_company_names	company_urls	start_dates	end_dates	related_skils_in_job	positions	locations	responsibilities	extra_curricular_activity_types	extra_curricular_organization_names	extra_curricular_organization_links	role_positions	languages	proficiency_levels	certification_providers	certification_skills	online_links	issue_dates	expiry_dates	job_position_name	educationaL_requirements	experiencere_requirement	age_requirement	responsibilities.1	skills_required	matched_score
0	NaN	Big data analytics working and database warehouse manager with robust experience in handling all kinds of data. I have also used multiple cloud infrastructure services and am well acquainted with them. Currently in search of role that offers more of development.	['Big Data', 'Hadoop', 'Hive', 'Python', 'Mapreduce', 'Spark', 'Java', 'Machine Learning', 'Cloud', 'Hdfs', 'YARN', 'Core Java', 'Data Science', 'C++', 'Data Structures', 'DBMS', 'RDBMS', 'Informatica', 'Talend', 'Amazon Redshift', 'Microsoft Azure']	['The Amity School of Engineering & Technology (ASET), Noida']	['B.Tech']	['2019']	['N/A']	[None]	['Electronics']	['Coca-COla']	[None]	['Nov 2019']	['Till Date']	[['Big Data']]	['Big Data Analyst']	['N/A']	Technical Support\nTroubleshooting\nCollaboration\nDocumentation\nSystem Monitoring\nSoftware Deployment\nTraining & Mentorship\nIndustry Trends\nField Visits\n\n\n\n\n	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Senior Software Engineer	B.Sc in Computer Science & Engineering from a reputed university.	At least 1 year	NaN	Technical Support\nTroubleshooting\nCollaboration\nDocumentation\nSystem Monitoring\nSoftware Deployment\nTraining & Mentorship\nIndustry Trends\nField Visits\n\n\n\n\n	NaN	0.850000
1	NaN	Fresher looking to join as a data analyst and junior data scientist. Experienced in creating meaningful data dashboards and evaluation models.	['Data Analysis', 'Data Analytics', 'Business Analysis', 'R', 'SAS', 'PowerBi', 'Tableau', 'Data Visualization', 'Business Analytics', 'Machine Learning']	['Delhi University - Hansraj College', 'Delhi University - Hansraj College']	['B.Sc (Maths)', 'M.Sc (Science) (Statistics)']	['2015', '2018']	['N/A', 'N/A']	['N/A', 'N/A']	['Mathematics', 'Statistics']	['BIB Consultancy']	['N/A']	['Sep 2019']	['Till Date']	[['Data Analysis', 'Business Analysis', 'Machine Learning']]	['Business Analyst']	['N/A']	Machine Learning Leadership\nCross-Functional Collaboration\nStrategy Development\nML/NLP Infrastructure\nPrototype Transformation\nML System Design\nAlgorithm Research\nApplication Development\nDataset Selection\nML Testing\nStatistical Analysis\nR&D in ML/NLP\nText Representation\nData Pipeline Design\nStatistical Data Analysis\nModel Training\nTeam Collaboration\nResearch Reporting\nAlgorithm Analysis	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Machine Learning (ML) Engineer	M.Sc in Computer Science & Engineering or in any relevant discipline from a reputed University	At least 5 year(s)	NaN	Machine Learning Leadership\nCross-Functional Collaboration\nStrategy Development\nML/NLP Infrastructure\nPrototype Transformation\nML System Design\nAlgorithm Research\nApplication Development\nDataset Selection\nML Testing\nStatistical Analysis\nR&D in ML/NLP\nText Representation\nData Pipeline Design\nStatistical Data Analysis\nModel Training\nTeam Collaboration\nResearch Reporting\nAlgorithm Analysis	NaN	0.750000
2	NaN	NaN	['Software Development', 'Machine Learning', 'Deep Learning', 'Risk Assessment', 'Requirement Gathering', 'Application Support', 'JavaScript', 'Python', 'Docker', 'HTML', 'Hive', 'CSS', 'C', 'C++']	['Birla Institute of Technology (BIT), Ranchi']	['B.Tech']	['2018']	['N/A']	['N/A']	['Electronics/Telecommunication']	['Axis Bank Limited']	['N/A']	['June 2018']	['Till Date']	[['Unified Payment Interface', 'Risk Prediction', 'Big Data', 'Spark', 'PySpark']]	['Software Developer (Machine Learning Engineer)']	['N/A']	Trade Marketing Executive\nBrand Visibility, Sales Targets\nField Marketing, Campaigns, Product Distribution\nBrand Head\nExcel, KPIs Tracking	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Executive/ Senior Executive- Trade Marketing, Hygiene Products	Master of Business Administration (MBA)	At least 3 years	NaN	Trade Marketing Executive\nBrand Visibility, Sales Targets\nField Marketing, Campaigns, Product Distribution\nBrand Head\nExcel, KPIs Tracking	Brand Promotion\nCampaign Management\nField Supervision\nMerchandising\npromotional activities\nTrade Marketing	0.416667
3	NaN	To obtain a position in a fast-paced business office environment, demanding a strong organizational, technical, and interpersonal position utilizing my skills and attributes.	['accounts payables', 'accounts receivables', 'Accounts Payable', 'Accounts Receivable', 'administrative functions', 'trial balance', 'banking', 'budget', 'bi', 'closing', 'Computer Applications', 'Credit', 'clients', 'Customer Service', 'data entry', 'delivery', 'driving', 'email', 'insurance', 'inventory', 'ledger', 'Access', 'Excel', 'Outlook', 'PowerPoint', 'Word', 'mortgage loan', 'Enterprise', 'policies', 'QuickBooks', 'Sales', 'sales reports', 'telecommunications', 'phone', 'workflow', 'written']	['Martinez Adult Education, Business Training Center ï¼ City , State']	['Computer Applications Specialist Certificate Program']	['2008']	[None]	[None]	['Computer Applications']	['Company Name ï¼ City , State', 'Company Name ï¼ City , State', 'Company Name ï¼ City , State', 'Company Name ï¼ City , State', 'Company Name ï¼ City , State', 'N/A']	[None, None, None, None, None, None]	['January 2011', 'January 2008', 'January 2006', 'January 2004', 'January 2001', 'N/A']	['November 2015', 'January 2010', 'January 2008', 'January 2006', 'January 2004', None]	[['accounts receivables', 'banking', 'G/L Accounts', 'accounts payables', 'credit cards', 'reconcile', 'commission reports', 'credit checks', 'customer service', 'international travel'], ['data entry', 'accounts receivable', 'cash handling', 'customer communication', 'inventory reports', 'problem-solving'], ['mortgage processing', 'analytical aptitude', 'credit reports', 'customer communication'], ['commercial auto underwriting', 'data entry', 'application review', 'customer communication'], ['personal auto underwriting', 'data entry', 'policy review', 'customer service'], ['training', 'medical record review', 'data entry', 'document design', 'customer service', 'team performance']]	['Accountant', 'Accounts Receivable Clerk', 'Mortgage Underwriter', 'Commercial Auto Underwriter', 'Personal Auto Underwriter', 'Claims Examiner']	['City, State', 'City, State', 'City, State', 'City, State', 'City, State', 'N/A']	Apparel Sourcing\nQuality Garment Sourcing\nReliable Partner\nBuyer/Vendor Communication	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Business Development Executive	Bachelor/Honors	1 to 3 years	Age 22 to 30 years	Apparel Sourcing\nQuality Garment Sourcing\nReliable Partner\nBuyer/Vendor Communication	Fast typing skill\nIELTSInternet browsing & online work ability.	0.760000
4	NaN	Professional accountant with an outstanding work ethic and integrity seeking to make a valuable contribution utilizing strong analytical, organizational, communication, and computer skills.	['Analytical reasoning', 'Compliance testing knowledge', 'Effective time management', 'Public and private accounting', 'accounting', 'accounting systems', 'accounts payable', 'accounts receivable', 'administrative', 'AR', 'billing', 'closing', 'client', 'clients', 'documentation', 'financial', 'financial reports', 'preparation of financial reports', 'Preparation of financial statements', 'fixed assets', 'managing', 'month-end closing', 'policies', 'maintain records', 'reporting', 'Research', 'sales', 'tax', 'taxes', 'tax returns', 'annual reports', 'year-end']	['Kent State University']	['Bachelor of Business Administration']	[None]	['3.84']	[None]	['Accounting']	['Company Name', 'Company Name', 'Company Name', 'Company Name', 'Company Name']	[None, None, None, None, None]	['January 2016', 'January 2016', 'January 2012', 'January 2009', 'January 2006']	['Current', 'January 2016', 'January 2015', 'January 2011', 'January 2009']	[['collections', 'accounts receivable', 'financial reports', 'AR aging', 'customer queries', 'sales and use tax audits'], ['financial statements', 'GAAP', 'asset', 'liability', 'capital account', 'accounting controls', 'audits'], ['sales tax', 'tax returns', 'business licenses', 'annual reports', 'tax audits'], ['financial reporting', 'fixed assets', 'sales tax', 'cash projections', 'general ledger accounting'], ['audit procedures', 'substantive tests', 'internal accounting', 'tests of compliance', 'audit programs']]	['Staff Accountant', 'Senior Accountant', 'Tax Analyst', 'Staff Accountant II', 'Staff Auditor II']	['City, State', 'City, State', 'City, State', 'City, State', 'City, State']	iOS Lifecycle\nRequirement Analysis\nNative Frameworks\niOS Development\nAPI Integration\nTechnical Communication\nUI Design\nPerformance Optimization\nFeature Collaboration\nBug Fixing\nCode Translation\nHigh-Performance Development\nTask Management\nCross-Team Collaboration\nCode Quality	['Professional Organization', 'Honor Society', 'Honor Society', 'Honor Society']	['Ohio Society of CPAs', 'Beta Alpha Psi', 'Golden Key International Honour Society', 'Beta Gamma Sigma']	[None, None, None, None]	[None, None, None, None]	NaN	NaN	['Ohio Notary Public']	[None]	[None]	[None]	['February 15, 2021']	Senior iOS Engineer	Bachelor of Science (BSc) in Computer Science	At least 4 years	NaN	iOS Lifecycle\nRequirement Analysis\nNative Frameworks\niOS Development\nAPI Integration\nTechnical Communication\nUI Design\nPerformance Optimization\nFeature Collaboration\nBug Fixing\nCode Translation\nHigh-Performance Development\nTask Management\nCross-Team Collaboration\nCode Quality	iOS\niOS App Developer\niOS Application Development\niOS Development\nMobile apps Developer (iOS)\nNative IOS\nSwift (iOS)\nSwift UI	0.650000
5	NaN	To secure an IT specialist, desktop support, network administration, database administrator, technical support specialist or related position with a growing organization where my Microsoft certification, technical aptitude, networking, Windows and Mac OS, Apple and Android IOS, web development, application development, Linux, Microsoft applications, managing, testing, client support, help desk, technical support, troubleshooting, and leadership skills can benefit those who I work for as well as myself.	['Microsoft Applications', 'Network Security', 'Networking', 'PC hardware and software installation, configuration, and troubleshooting', 'Remote Desktop and Help Desk Management', 'Verbal Communication', 'Technical Support', 'Team Leadership', 'Programming Languages', 'On-call tech support', 'Windows & Mac OS', 'Wiring/Wire Spicing: Cat3, Cat5, Cat5e, Coaxial', 'Management', 'VoIP, TCP/IP, IPSec, ATM, SS7, IPX, DNS, BIND, DHCP, HSRP and LAN/WAN architecture', 'Application Development', 'Voice Over IP Telephone', 'Inventory Management']	['Glen Oaks High School', 'Glen Oaks High School']	['Bachelor Degree', 'Associate Degree']	[None, None]	[None, None]	[None, None]	['Electronics and Communications Engineering Technology', 'Software Development']	['N/A', 'Company Name', 'Company Name']	[None, None, None]	['August 2006', 'August 2013', 'July 2014']	['January 2013', 'September 2014', 'Current']	[['Microsoft Applications', 'Windows Applications', 'Mac OS and IOS', 'Network routers', 'Cisco ASA firewall', 'Juniper Net-screen', 'LANs, WANs', 'Cloud Experience'], ['Microsoft applications', 'Windows and Mac OS', 'Linux', 'Web Development'], ['Customer Support', 'Technical Support', 'Network Administration', 'Inventory Management']]	['Engineering Systems Installer', 'IT Technician/QA Tester', 'Installation/Service Technician']	['City, State', 'City, State', 'N/A']	Machine Learning Design\nData Analysis\nModel Training\nAI Integration\nInnovation\nCross-Functional Collaboration\nModel Deployment\nDocumentation\nAnalytical Skills\nCommunication\nTeam Collaboration	NaN	NaN	NaN	NaN	NaN	NaN	['Microsoft Certification']	[None]	[None]	['May 2013']	[None]	AI Engineer	Bachelors or Masters degree in Computer Science, Engineering, or a related field.	NaN	NaN	Machine Learning Design\nData Analysis\nModel Training\nAI Integration\nInnovation\nCross-Functional Collaboration\nModel Deployment\nDocumentation\nAnalytical Skills\nCommunication\nTeam Collaboration	Python\nR or Java\nTensorFlow\nPyTorch\nScikit-learn.	0.850000
6	NaN	NaN	['Machine Learning', 'Linear Regression', 'Ridge Regression', 'Lasso Regression', 'Tableau', 'Time Series Analysis']	['DJR College and University']	['B.Tech']	['2020']	['N/A']	['N/A']	['IT']	['Remiro Amio']	['N/A']	['Jan 2019']	['Sep 2019']	[None]	['Intern']	['N/A']	iOS Lifecycle\nRequirement Analysis\nNative Frameworks\niOS Development\nAPI Integration\nTechnical Communication\nUI Design\nPerformance Optimization\nFeature Collaboration\nBug Fixing\nCode Translation\nHigh-Performance Development\nTask Management\nCross-Team Collaboration\nCode Quality	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Senior iOS Engineer	Bachelor of Science (BSc) in Computer Science	At least 4 years	NaN	iOS Lifecycle\nRequirement Analysis\nNative Frameworks\niOS Development\nAPI Integration\nTechnical Communication\nUI Design\nPerformance Optimization\nFeature Collaboration\nBug Fixing\nCode Translation\nHigh-Performance Development\nTask Management\nCross-Team Collaboration\nCode Quality	iOS\niOS App Developer\niOS Application Development\niOS Development\nMobile apps Developer (iOS)\nNative IOS\nSwift (iOS)\nSwift UI	0.650000
7	NaN	NaN	['Maintenance', 'Corrective Maintenance', 'Documentation', 'Industrial Machinery', 'Preventive Maintenance', 'Sensors', 'Biotechnology', 'Electrical Mechanical', 'Estimation', 'Hydraulics', 'Mechanical Technician', 'Pneumatics', 'Project Manager', 'Sop', 'Manufacturing Process', 'Apqp', 'Assembly', 'Circuit Boards', 'Dmm', 'Electrical Test', 'Esd', 'First Article Inspection', 'Inspection', 'Medical Devices', 'Oscilloscope', 'Production Process', 'Schematic', 'Soldering', 'Surface Mount', 'Test Engineer', 'Through-hole', 'Wiring', 'Calibration', 'Control Systems', 'Packaging', 'Process Control', 'Sensor', 'Temperature', 'And Humidity', 'Control System Design', 'Electrical Engineer', 'Engineer', 'Entry Level', 'Ieee', 'Mechanical/electrical Engineer', 'Proactive', 'Self Motivated', 'Testing', 'Training', 'Electrical Engineering', 'Pr', 'Public Relations']	['POLYTECHNIC UNIVERSITY OF PUERTO RICO']	['Bachelor of Science']	['2009']	['2.50']	['GPA']	['Electrical Engineering']	['Company Name', 'Company Name', 'Company Name']	[None, None, None]	['January 2013', 'January 2011', 'January 2005']	['Current', 'January 2012', 'January 2011']	[['Electrical Test', 'Soldering', 'Wiring', 'Troubleshooting'], ['Calibration', 'Troubleshooting', 'Preventive Maintenance', 'Installation'], ['Project Estimation', 'Corrective Maintenance', 'Preventive Maintenance', 'Installation']]	['Engineering Technician', 'Instrument Technician', 'Project Manager Assistance']	['City, State', 'City, State', 'City, State']	iOS Lifecycle\nRequirement Analysis\nNative Frameworks\niOS Development\nAPI Integration\nTechnical Communication\nUI Design\nPerformance Optimization\nFeature Collaboration\nBug Fixing\nCode Translation\nHigh-Performance Development\nTask Management\nCross-Team Collaboration\nCode Quality	NaN	NaN	NaN	NaN	['Spanish']	['N/A']	['AIT']	[None]	[None]	[None]	[None]	Senior iOS Engineer	Bachelor of Science (BSc) in Computer Science	At least 4 years	NaN	iOS Lifecycle\nRequirement Analysis\nNative Frameworks\niOS Development\nAPI Integration\nTechnical Communication\nUI Design\nPerformance Optimization\nFeature Collaboration\nBug Fixing\nCode Translation\nHigh-Performance Development\nTask Management\nCross-Team Collaboration\nCode Quality	iOS\niOS App Developer\niOS Application Development\niOS Development\nMobile apps Developer (iOS)\nNative IOS\nSwift (iOS)\nSwift UI	0.650000
8	NaN	Certified Data analyst with a degree in Electronics Engineering, I have hands on experience in analyzing & interpreting data with good numerical accuracy.	['Python', 'Machine Learning', 'MySQL', 'Data Mining', 'Deep Learning', 'Data Analysis', 'Computer Vision', 'Flask API', 'Predictive Modeling', 'AWS', 'Scikit-Learn', 'Numpy', 'Statistical Analysis', 'Multivariate Analysis', 'Decision Trees', 'Random Forest', 'Xgboost', 'NLP']	['Nagpur University']	['B.Tech/B.E.']	['2019']	['N/A']	['N/A']	['Electronics/Telecommunication']	['AMZ Loans and Mortgages ERC Analytics']	['N/A']	['Jun 2019']	['till date']	[['Data Analysis', 'Employee Satisfaction', 'HR Collaboration']]	['Associate Analyst']	['N/A']	Machinery Maintenance\nTroubleshooting\nReport Preparation\nLog Maintenance	NaN	NaN	NaN	NaN	NaN	NaN	['Google Cloud']	[None]	[None]	[None]	[None]	Mechanical Engineer	Bachelor of Science (BSc) in Mechanical Engineering, Diploma in Mechanical	2 to 5 years	Age 25 to 40 years	Machinery Maintenance\nTroubleshooting\nReport Preparation\nLog Maintenance	Maintenance and Troubleshooting\nMechanical	0.550000
9	NaN	NaN	['Django', 'Python', 'Relational databases', 'RestAPI', 'Github', 'Jira', 'PostgreSQL', 'Software development', 'Debugging', 'Machine learning', 'Natural language Processing', 'Artificial Intelligence', 'Data Analysis', 'Docker', 'Tornado', 'Software Developer', 'Project Management']	['Dr. Virendra Swaroop Institute of Computer Studies, Kanpur', 'Pranveer Singh Institute of Technology, Kanpur']	['BCA', 'MCA']	['2016', '2019']	[None, None]	[None, None]	['Computers', 'Computers']	['Daffodil Software Pvt Ltd']	[None]	['Jan 2019']	['Till Date']	[['Developing', 'designing', 'optimizing automation script', 'crons', 'managing database conflicts', 'executing solution']]	['Software Developer']	['N/A']	Apparel Sourcing\nQuality Garment Sourcing\nReliable Partner\nBuyer/Vendor Communication	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Business Development Executive	Bachelor/Honors	1 to 3 years	Age 22 to 30 years	Apparel Sourcing\nQuality Garment Sourcing\nReliable Partner\nBuyer/Vendor Communication	Fast typing skill\nIELTSInternet browsing & online work ability.	0.383333

	address	career_objective	skills	educational_institution_name	degree_names	passing_years	educational_results	result_types	major_field_of_studies	professional_company_names	company_urls	start_dates	end_dates	related_skils_in_job	positions	locations	responsibilities	extra_curricular_activity_types	extra_curricular_organization_names	extra_curricular_organization_links	role_positions	languages	proficiency_levels	certification_providers	certification_skills	online_links	issue_dates	expiry_dates	job_position_name	educationaL_requirements	experiencere_requirement	age_requirement	responsibilities.1	skills_required	matched_score
7625	NaN	NaN	['PLC', 'IEC 61131 (Ladder Logic, Functional Block Diagram, Structured Text, Instruction List.)', 'Java', 'C', 'Visual Basic', 'VHDL', 'PSpice', 'Assembly (Intel, Motorola, TI)', 'Labview', 'AutoCAD', 'Inventor', 'Matlab', 'Microsoft Office', 'PSIM', 'Easy Power', 'Xilinx ISE', 'Printed Circuit Board CAD (Protel)', 'Siemens Step 7', 'Wago CoDeSys', 'Allen Bradley RSLogix', 'ERP (Alliance, Global Shop, XA, SAP)', 'API', 'automation', 'budgeting', 'cabling', 'CAD', 'Conversion', 'client', 'clients', 'DC', 'Designing', 'flash', 'Functional', 'hardware design', 'HP', 'HVAC', 'instruction', 'Intel', 'microprocessor', 'Modeling', 'Motorola', 'Power distribution', 'power generation', 'processes', 'Programming', 'proposals', 'Renovation', 'renovations', 'safety', 'scheduling', 'schematics', 'Siemens', 'simulation', 'staff management', 'Structured', 'Supervising', 'switchgear', 'tender', 'troubleshoot', 'troubleshooting', 'validation']	['University of Oklahoma']	['Bachelor of Science']	['2006']	['3.73']	['GPA']	['Electrical and Computer Engineering']	['Company Name', 'Company Name', 'Company Name', 'Company Name', 'Company Name']	[None, None, None, None, None]	['December 2014', 'May 2011', 'October 2008', 'August 2006', 'January 2005']	['Current', 'December 2014', 'May 2011', 'October 2008', 'August 2006']	[['PLC', 'ERP', 'AutoCAD', 'staff management', 'budgeting', 'scheduling'], ['Modeling', 'hardware design', 'simulation', 'verification', 'validation'], ['Designing', 'supervising', 'technical studies', 'trouble shooting'], ['power distribution', 'construction', 'HVAC'], ['design', 'implementation', 'microprocessor']]	['Engineering Supervisor', 'Electrical Design Engineer', 'Project Engineer', 'Facility Engineer', 'Automation Engineer, Intern']	['City , State', 'City , State', 'City , State', 'City , State', 'City , State']	Machinery Maintenance\nTroubleshooting\nReport Preparation\nLog Maintenance	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Mechanical Engineer	Bachelor of Science (BSc) in Mechanical Engineering, Diploma in Mechanical	2 to 5 years	Age 25 to 40 years	Machinery Maintenance\nTroubleshooting\nReport Preparation\nLog Maintenance	Maintenance and Troubleshooting\nMechanical	0.826667
7626	NaN	Machine Learning Engineer seeking assignments Deep Learning, Reinforcement Learning, Tensorflow. Keras.	['Artificial Intelligence', 'Deep Learning', 'Reinforcement Learning', 'Tensorflow Keras', 'Scikit learn', 'Numpy', 'Pandas', 'Matplotlib']	['IIIT D&M Kancheepuram, Chennai']	['B.Tech(IT)']	['2019']	['N/A']	['N/A']	['Information Technology']	['Larsen & Toubro']	['N/A']	['Dec 2019']	['Till Date']	[['OCR', 'Machine Learning']]	['Analyst Intern']	['N/A']	Technical Support\nTroubleshooting\nCollaboration\nDocumentation\nSystem Monitoring\nSoftware Deployment\nTraining & Mentorship\nIndustry Trends\nField Visits\n\n\n\n\n	['Scholarship']	['KSST']	['N/A']	['Scholar']	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Senior Software Engineer	B.Sc in Computer Science & Engineering from a reputed university.	At least 1 year	NaN	Technical Support\nTroubleshooting\nCollaboration\nDocumentation\nSystem Monitoring\nSoftware Deployment\nTraining & Mentorship\nIndustry Trends\nField Visits\n\n\n\n\n	NaN	0.816667
7627	NaN	NaN	['Data Science', 'Data Analysis', 'Machine Learning', 'Deep Learning', 'Statistical Analysis', 'Data Mining', 'Computer Vision', 'Algorithm Development', 'Linear Regression', 'Market Basket Analysis', 'Python', 'R', 'SQL', 'NoSQL']	['Delhi College of Art', 'Aegis School of Business & Data Science']	['B.Com', 'PG Diploma']	['2017', '2019']	['N/A', 'N/A']	['N/A', 'N/A']	['Commerce', 'Data Science and Business Analytics']	['HP', 'Adnet Global']	[None, None]	['Feb 2020', 'Jan 2019']	['till date', 'Dec 2019']	[['Python'], ['Python', 'OpenCV', 'dlib']]	['Lead Data Scientist', 'Data Scientist']	['N/A', 'N/A']	Administrative Support\nScheduling\nFiling & Documentation\nCommunication\nTeam Support\nEquipment Maintenance\nInformation Provision\nInventory Management\nTeam Collaboration\nOHS Policy Development\nSafety Advice\nRisk Assessment\nPolicy Review\nOHS Training\nSafety Inspections\nUnsafe Act Prevention\nIncident Investigation\nReport Preparation	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Asst. Manager/ Manger (Administrative)	Bachelor/Honors	At least 5 years	Age at least 28 years	Administrative Support\nScheduling\nFiling & Documentation\nCommunication\nTeam Support\nEquipment Maintenance\nInformation Provision\nInventory Management\nTeam Collaboration\nOHS Policy Development\nSafety Advice\nRisk Assessment\nPolicy Review\nOHS Training\nSafety Inspections\nUnsafe Act Prevention\nIncident Investigation\nReport Preparation	•Administration\n•Health Safety and Environment\n•Safety and Security Management	0.650000
7628	NaN	NaN	['Microsoft Office', 'Advanced Excel', 'PowerPoint', 'MS Access', 'Atlas', 'Windows 95 - XP', 'Harvard Graphics', 'QuickBooks', 'NGS Systems', 'People Soft', 'SAP', 'Quicken', 'Nanovision', '2-Tier', 'Phoenix (E1)', 'Business Objects', 'Account reconciliations', 'Accounting', 'Accounts payable', 'Accounts receivables', 'Accruals', 'Ad hoc', 'AP', 'Balance sheet', 'Balance', 'Bank reconciliations', 'Billing', 'Bookkeeping', 'Budgeting', 'Budget', 'Charts', 'Oral communication', 'CPA', 'Databases', 'Fluent in English', 'Senior management', 'Finance', 'Financial analysis', 'Financial reports', 'Financial Reporting', 'Financial statements', 'Fixed assets', 'Forecasting', 'General ledger', 'General ledger accounts', 'Government', 'Graphs', 'JDE', 'Lotus Notes', 'Presentations', 'QuickBooks', 'Quicken', 'Reporting', 'SAP', 'SOX compliance', 'Sarbanes Oxley', 'Spanish', 'Tax', 'Variance analysis', 'Excellent written']	['Pepperdine University, Graziadio School of Business and Management', 'University of New Orleans']	['Master of Business Administration', 'Bachelor of Science']	['2008', '2004']	['N/A', 'N/A']	['N/A', 'N/A']	['Business Development Strategy, Management', 'Finance Spanish']	['Company Name', 'Company Name', 'Company Name', 'Company Name', 'Company Name', 'Company Name']	[None, None, None, None, None, None]	['July 2015', 'July 2008', 'December 2007', 'October 2005', 'June 2005', 'September 2004']	['Current', 'July 2015', 'June 2008', 'October 2007', 'August 2005', 'January 2005']	[None, None, None, None, None, None]	['Supervisor Accountant', 'Lead Accountant', 'Senior Accountant', 'Senior Financial Analyst', 'General Accountant', 'Accountant']	['City, State', 'City, State', 'City, State', 'City, State', 'City, State', 'City, State']	Database Design & Development\nSQL Query Optimization\nData Integrity & Security\nBI Solutions Development\nETL Process Implementation\nDatabase Maintenance\nBackup & Restore Management\nIndex Rebuilding & Performance Tuning\nSQL Server Clustering & High Availability\nSQL Server Replication\nHigh Availability Group Management\nDatabase Monitoring & Troubleshooting	NaN	NaN	NaN	NaN	['English', 'Spanish']	['Fluent', 'Decreased proficiency due to moderate use']	NaN	NaN	NaN	NaN	NaN	Database Administrator (DBA)	Bachelor of Science (BSc) in Computer Science	At least 1 year	Age 25 to 40 years	Database Design & Development\nSQL Query Optimization\nData Integrity & Security\nBI Solutions Development\nETL Process Implementation\nDatabase Maintenance\nBackup & Restore Management\nIndex Rebuilding & Performance Tuning\nSQL Server Clustering & High Availability\nSQL Server Replication\nHigh Availability Group Management\nDatabase Monitoring & Troubleshooting	ASP.NET MVC Strong understanding of database design\nDatabase Administrator (DBA)\nDatabase management\nElasticsearch\nMongoDB\nMySQL database\nNoSQL database\nREDIS	0.676667
7629	160 Bleecker Street, Apartment 4DW New York, NY 10012	NaN	['Game Theory', 'Bounded Rationality', 'Cryptography', 'Algorithms', 'Social Networks', 'Analysis of Algorithms', 'Combinatorial Optimizations']	['Cornell University, Department of Computer Science', 'Tel Aviv University, Israel']	['PhD Candidate, Computer Science', 'B.Sc., Double major in Computer Science and Management']	['Present', '2008']	['4.1', 'Summa cum laude']	['GPA', 'N/A']	['Computer Science', 'Computer Science and Management']	['Google, AdX group', 'Google, Google Research', 'Intel, Mobile Wireless Group']	[None, None, None]	['Summer 2013', 'Summer 2012', '2007']	[None, None, '2010']	[['Product Development', 'Algorithm Design', 'Front-end Development'], ['Theoretical Research', 'Data Experiments'], ['Firmware Development', 'Design and Implementation', 'Integration Testing']]	['Summer Intern', 'Summer Intern', 'FW Developer']	['New York, NY', 'Mountain View, CA', 'Petach Tikva, Israel']	Mushak Forms Maintenance\nVAT Software & MS Office Maintenance\nVAT Ledger Management\nVAT Challan Coordination\nLiaison with VAT Office\nVAT Documentation\nOther Assigned Tasks	['Volunteering', 'Volunteering']	['Intel Israel volunteer network', 'Tel Aviv University']	[None, None]	['Math tutor', 'CCNA teacher']	['English', 'Hebrew']	['Fluent', 'Native']	NaN	NaN	NaN	NaN	NaN	Executive - VAT	BBA in Accounting and Finance	1 to 3 years	NaN	Mushak Forms Maintenance\nVAT Software & MS Office Maintenance\nVAT Ledger Management\nVAT Challan Coordination\nLiaison with VAT Office\nVAT Documentation\nOther Assigned Tasks	VAT and Tax	0.650000
7630	Trenton, NJ	Dedicated and well-rounded software engineer with a passion for math, computer science, and elite education. Bringing 12+ years of teaching expertise to help Amazon's Career Fulfillment students feel the same joy and pride in my comprehensive JavaScript computer science program as my past 150+ high school students. Eager to teach the basics, get students the tools they need, and foster their passion to reach their goals.	['HTML', 'CSS', 'JavaScript (Angular)', 'Python', 'Git', 'Algorithm Design', 'Technical Writing', 'Data Analysis', 'Interpersonal Communication', 'Patience']	['Princeton International School of Mathematics and Science', 'New York University']	['Master of Science', 'Bachelor of Science']	['2015', '2012']	['N/A', 'N/A']	[None, None]	['Computer Science', 'Computer Science']	['Princeton International School of Mathematics and Science', 'HKA Enterprises', 'Academy of Mine']	['N/A', 'N/A', 'N/A']	['2017', '2014', '2012']	['current', '2017', '2014']	[['JavaScript', 'Teaching', 'Program Development'], ['Technical Support', 'Database Design', 'Software Testing'], ['Agile/Scrum', 'Code Writing', 'CICD']]	['Computer Science Teacher', 'Software Engineer', 'Junior Software Engineer']	['Princeton, NJ', 'Remote', 'Cherry Hill, NJ']	15+ Years Banking Experience\nAudit/Inspection/ICC Leadership\nInternal Audit & Compliance\nRisk-Based Operational Reviews\nSystem-Based Audit Expertise\nKnowledge of Bangladeshi Laws	NaN	NaN	NaN	NaN	NaN	NaN	['AWS', 'CSM']	[None, None]	[None, None]	[None, None]	[None, None]	Head of Internal Control & Compliance (ICC) - SEVP/DMD	Masters, Master of Business Administration (MBA), Master of Business Management (MBM)	At least 15 years	Age at most 52 years	15+ Years Banking Experience\nAudit/Inspection/ICC Leadership\nInternal Audit & Compliance\nRisk-Based Operational Reviews\nSystem-Based Audit Expertise\nKnowledge of Bangladeshi Laws	AUDIT AND INSPECTION\nBanking\nInternal Audit	0.643333
7631	NaN	Want to work as a Machine Learning Production Engineer.	['Machine Learning Engineer', 'Data Analyst', 'Natural Language Processing', 'Deep Learning', 'Reinforcement Learning', 'Tensorflow', 'Keras', 'Scikit Learn', 'Numpy', 'Pandas', 'Matplotlib', 'Python']	['AMIT, Bijnor', 'KVIT, Pilani']	['B.Tech', 'M.Tech']	['2017', 'N/A']	['N/A', 'Gold Medalist']	['N/A', 'N/A']	['Electronics/Telecommunication', 'Advanced Analytics']	['Larsen & Toubro']	[None]	['Jul 2019']	['N/A']	[None]	['Analyst Intern']	['N/A']	Data Platform Design\nData Pipeline Development\nETL Processes\nSQL Query Optimization\nData Integration & Transformation\nData Modeling\nStakeholder Collaboration\nData Quality Monitoring\nContinuous Learning	['Research Assistant & Teaching Assistant']	['KVIT']	[None]	['N/A']	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Data Engineer	Bachelor of Science (BSc)	5 to 8 years	NaN	Data Platform Design\nData Pipeline Development\nETL Processes\nSQL Query Optimization\nData Integration & Transformation\nData Modeling\nStakeholder Collaboration\nData Quality Monitoring\nContinuous Learning	Azure\nBig Data\nData Analytics\nETL Tools\nPower BI\nSQL	0.650000
7632	NaN	Software developer focused in the areas of Machine learning application development. Motivated to learn, grow and excel my experience by challenging myself.	['Software Engineer', 'Data Analyst', 'Machine Learning', 'Text Analytics', 'Software Development', 'Object Oriented Programming', 'Pandas', 'Numpy', 'Java', 'Python', 'SpringBoot', 'Laravel']	['Dr. Jagjiban Rao Engineering College']	['B.Tech']	['2019']	[None]	[None]	['ECE']	['KLP Technology Solutions']	[None]	['Jan 2019']	['till date']	[['Machine Learning', 'Software Engineering', 'Feature Engineering', 'Evaluation Methods']]	['Software Engineer']	['N/A']	Mikrotik Router Configuration\nOLT Device Setup & Management\nIntegration with Billing Software\nNetwork Monitoring Tools Integration\nConnectivity Troubleshooting\nTechnical Support & Escalation\nInstallation & Configuration\nGPON/EPON Expertise\nCisco, OLT, MikroTik Knowledge	['UI/UX Development']	['OnDevice Machine Learning']	[None]	[None]	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Network Support Engineer	Diploma, Bachelor/Honors	At least 3 years	Age 25 to 35 years	Mikrotik Router Configuration\nOLT Device Setup & Management\nIntegration with Billing Software\nNetwork Monitoring Tools Integration\nConnectivity Troubleshooting\nTechnical Support & Escalation\nInstallation & Configuration\nGPON/EPON Expertise\nCisco, OLT, MikroTik Knowledge	CCNA (Cisco Certified Network Associate)\nGPON\nHardware & Networking\nIIG\nISP\nIT Enabled services\nOLT and ONU	0.760000
7633	NaN	NaN	['Accounting', 'Accounting Software', 'Accounts Payables', 'accounts payable', 'Accounts Receivables', 'accrual', 'administrative', 'administrative support', 'Trial Balance', 'balance', 'Balance Sheet', 'bank reconciliation', 'Bank Reconciliation', 'Bookkeeping', 'book', 'c', 'Driving License', 'Cash Management', 'closing', 'computer peripherals', 'Credit', 'Clients', 'Data Entry', 'email', 'English', 'ERP', 'Finance', 'Financials', 'Financial', 'Fixed Assets', 'Funds', 'General Ledger', 'Hindi', 'Insurance', 'Internet applications', 'Invoicing', 'Languages', 'law', 'legal', 'letters', 'Managing', 'Excel', 'MS Office applications', 'office', 'Outlook', 'PowerPoint', 'Word', 'MIS', 'MYOB', 'negotiating', 'Payroll', 'policies', 'Purchasing', 'QuickBooks', 'Sales', 'sales analysis', 'Secretarial', 'Sound', 'strategy', 'supervision', 'typing speed', 'Xpert']	['University of Kerala', 'Mahatma Gandhi University']	['M.Com (Master of Commerce)', 'B.Com (Bachelor of Commerce)']	['N/A', 'N/A']	['N/A', 'N/A']	['N/A', 'N/A']	['N/A', 'N/A']	['Company Name', 'Company Name', 'Company Name', 'Company Name']	[None, None, None, None]	['October 2012', 'April 2011', 'July 2008', 'August 2005']	['October 2014', 'October 2012', 'April 2011', 'July 2008']	[['Accounts Payables', 'Financial Transactions', 'Accounting'], ['Accounts Payables', 'Bank Reconciliation', 'Financial Reports'], ['Invoicing', 'Accounts Receivables', 'Payroll'], ['Bookkeeping', 'Bank Reconciliation', 'Sales']]	['Accountant', 'Accountant - Payables / Accounting Clerk', 'Junior Accountant', 'Accountant cum Secretary']	['City', 'City', 'City', 'City']	Machinery Maintenance\nTroubleshooting\nReport Preparation\nLog Maintenance	NaN	NaN	NaN	NaN	['English', 'Hindi', 'Malayalam']	['N/A', 'N/A', 'N/A']	NaN	NaN	NaN	NaN	NaN	Mechanical Engineer	Bachelor of Science (BSc) in Mechanical Engineering, Diploma in Mechanical	2 to 5 years	Age 25 to 40 years	Machinery Maintenance\nTroubleshooting\nReport Preparation\nLog Maintenance	Maintenance and Troubleshooting\nMechanical	0.350000
7634	NaN	NaN	['Electronic & Mechanical Technology', 'Maintenance Management', 'Project Management', 'Program Management', 'Lean/six sigma principles', 'Mechanical Component Troubleshooting', 'Quality Assurance/Control', 'Material Management', 'Staff Development/Leadership', 'Technical Interface', 'Safety Compliance', 'Microsoft Office', 'Microsoft Project', 'Microsoft Access', 'SAP', 'SharePoint']	['Eastern New Mexico University', 'Embry-Riddle Aeronautical University', 'Enterprise Community College']	['MBA', 'Bachelor of Science', 'Associate of Science']	['2015', '2008', '2004']	['N/A', 'N/A', 'N/A']	['N/A', 'N/A', 'N/A']	['N/A', 'Professional Aeronautics', 'Airframe & Power Plant Technology']	['Company Name', 'Company Name', 'Company Name']	[None, None, None]	['02/2018', '08/2011', '03/2001']	['Current', '02/2018', 'Current']	[['Project Management', 'Engineering', 'Technical Coordination'], ['Aircraft Maintenance', 'Technical Support', 'Engineering Change Management', 'Project Planning'], ['Aircraft Maintenance', 'Technical Supervision', 'Quality Control', 'System Calibration']]	['Engineering Project Manager III', 'Field Engineer/Maintenance Support Engineer', 'Aircraft Mechanic / Electrician / Avionics Technician']	['City, State', 'City, State', 'State']	Machinery Maintenance\nTroubleshooting\nReport Preparation\nLog Maintenance	NaN	NaN	NaN	NaN	NaN	NaN	['FCC', 'A&P']	[None, None]	[None, None]	['N/A', 'N/A']	[None, None]	Mechanical Engineer	Bachelor of Science (BSc) in Mechanical Engineering, Diploma in Mechanical	2 to 5 years	Age 25 to 40 years	Machinery Maintenance\nTroubleshooting\nReport Preparation\nLog Maintenance	Maintenance and Troubleshooting\nMechanical	0.716667

Overview

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Common Values

Length

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(unknown)